and defibrillators, vascular grafts, hemodialyzers, catheters, circulatory support
devices and blood oxygenators), complex and challenging cardiovascular surgical
procedures (e.g. open-heart surgery), medical therapies (e.g. dialysis), valve
replacement problems (e.g. thromboembolism, hemolysis, paravalvular
regurgitation, endocarditis and structural failure of the valve); artificial heart
valves design (e.g. percutaneous or minimally invasive valve implantation and tissue
engineering), progress in stent technology to reduce restenosis and improvement of
stent outcome, development of pacemakers, cardioverter-defibrillator (AICD),
cardiac electrophysiologic devices from the perspectives of improved device
function, dual chamber activity, advances in technology and implantation
techniques, development of artificial lung for acute cardiopulmonary bypass and
improved biocompatibility.

**Artificial limbs:** The basic objective of prosthetics research is to design and develop artificial arms, hands and legs which can be used flexibly with physiological speeds-of response and strength and controlled almost without thought. The current state is basically a tool rather than a true limb replacement. The prosthesis is an interchangeable device that is worn and used as needed and then ignored. The major constraints of prostheses are weight, power, size and sufficient number of appropriate control sources to control the requisite number of degrees of freedom. The system requires better sensors, actuators and multifunctional control mechanisms. The basic building blocks of artificial limbs are mechatronics and robotics; current prosthetic components and interface techniques are still a long way from realizing the aforesaid objectives.

Dr. Muller is exploring the scope of various types of epidemic and pandemic outbreak and is suggesting a set of intelligent strategic moves as countermeasures. It is rational to adopt an efficient system; an optimal mix of e-governance (e.g. online grievance management system), broadcast communication protocol and artificial immune mechanism to fight against natural disaster, epidemic and pandemic outbreak. It is essential to analyze security, strategy, staff-resources and skill-style-support for efficient coordination and collaboration in corporate governance. There is threat of bio-terrorism on the soft targets (e.g. life-science supply chain and healthcare service chain). Is the conflict between security intelligence and business intelligence inevitable?

Epidemic and pandemic is a critical causal factor of poverty of human society globally. Can emerging technologies be used to fight against such calamities effectively? Dr. Muller is exploring the scope of epidemic and pandemic outbreak in depth; *Pandemic* is more dangerous than *epidemic*. When an epidemic spreads globally, it is called pandemic. In case of epidemic, a disease spreads at very fast rate within a particular period among one or more communities. For example, WHO has recently declared the outbreak of novel Corona virus as Pandemic, but it is controllable. There are other various types of threats of epidemic globally due to environmental pollution such as air, water, soil, light and sound pollution. The issues have been already discussed during session 2.

Epidemic may happen due to *air pollution* like dust at construction sites and industrial plants; smoke from vehicles; paste control problem (e.g. mosquitoes,
flies), malnutrition in slum areas, improper cleaning of garbages and stool of street animals? Epidemic may occur due to *water pollution* in supply of dirty drinking tap water caused by leakage in pipelines, contamination and jerms in water storage system, malfunctioning of tube wells, water filtering problem, mixing of water from drainage system and tap water pipeline, unprotected selling of unhealthy food (e.g. oily spicy biriyani) and beverages at retail outlets and by hawkers being contaminated by flies; risks of diahorrea, stomach upset and loose motion. Epidemic due to *soil pollution* and earthquake caused by random digging of soil for construction projects, erosion of soil at riverbeds; jamming in drains due to plastics, improper cleaning of drainage and sewage system; There is threat of epidemic due to *light pollution* in slum areas, unplanned urban development planning, blockage of sufficient sunlight into residential areas (e.g. houses, flats, multi-storied buildings). Epidemic due to *sound pollution* may be caused by playing loud and wild music, fireworks, activities at construction sites and industrial belts.

## 3. SYSTEM

### System Analytics

**Agents**: Doctors, scientists, system analysts, business analysts, engineers;

**Moves**: requirements engineering, system design, prototype testing, installation, testing, commissioning;

**Emerging technologies**: innovate a set of emerging technologies based on health security parameters and sustainable development goals.

* **Cancer prevention**

* Proactive approach

* Reactive approach

* Deep learning /* multi-model ensembling method */

* Raw data collection

* Data filtering

* Feature selection

* Cross validation

* Configure deep learning architecture

* Define training strategy(algorithm, learning rate, stopping criteria);

* Call 1st stage classification algorithm

* Call 2nd stage ensemble method

* Testing strategy KDD

* Intelligent reasoning

* Case based reasoning

* Perception common sense reasoning

* Regenerative medicine

* Precision medicine

* Cancer genomics

* **Biomedical technology**: artificial pancreas, artificial liver, artificial kidney, artificial limbs, artificial cardiovascular devices, oral insulin;

* **Artificial immune system** for epidemic and pandemic control.
Dr, Jim Morrison and Prof. Michel Bolton are presenting on emerging healthcare systems. Artificial intelligence (AI) is basically simulation of human intelligence. An intelligent reasoning system demands new data structure beyond knowledge base with envision, perception and proper assessment of a problem; reasoning is not effective when done in isolation from its significance in terms of the needs and interests of an agent with respect to the wider world. A rational reasoning system needs the support of an intelligent analytics. The basic objective is to evaluate the natural and adaptive immunity of a complex system. The evaluation of the immunity of a system involves modeling, defining complete specifications and verification.

First, it is essential to model the human biological system by proper representation of its various states and programs. Next, it is important to specify the properties of the system through logical reasoning. Finally, it is essential to develop a verification mechanism which justifies: does the model satisfy the properties indicating a healthy immune system? The evaluation of immunity of a system can be done by exhaustive search of the state space (local, global, initial and goal states and state transition relations) of a system through simulation, testing, deductive reasoning and model checking based on intelligent search. The procedure terminates with positive or negative answer; the positive answer indicates a healthy immune system; the negative results provide an error trace indicating incorrect modeling or specification of the system or the occurrence of malicious threats.

The human immune system is an adaptive, robust, complex and distributed information processing system which protects the health of the biological system from the attacks of malicious foreign pathogens (e.g. virus, bacteria, fungi, protozoa, parasitic worms). It discriminates the self from non-self elements. The immunity is either innate or adaptive; innate immunity detects and kills specific known invading organisms; adaptive immunity responds to previously unknown foreign organisms. AI community needs a new outlook, imagination and dreams to solve a complex problem like prevention of cancer through a set of simple mechanisms. There are some types of cancer due to bad luck. But, we still do not know enough about the causes and preventive measures of different types of cancer. The following section highlights two branches of artificial intelligence: (a) deep learning and (b) case based reasoning.

**Deep Learning** : Cancer is a complex global health problem involving abnormal cell growth and a major cause of morbidity and mortality. It is challenging to predict cancer using machine learning algorithms based on gene expression or image data for effective and accurate decision making, diagnosis and detection at early stage. It is basically a classification problem which predicts and distinguishes cancer patients from healthy persons. Recently, deep learning has been explored in terms of ensemble approach that combines multiple machine learning models. It is an interesting strategic option to apply various classification algorithms on informative gene expression and then a deep learning approach is employed to ensemble the outputs of the classifiers. This deep learning based multi-model ensemble method is an accurate and effective method for cancer prediction as compared to single
classifier. One of the critical issues is that it is irrational to mix data of various types
of cancer and then apply deep learning based multi-model ensemble method on this
mixed data. Rather, it is correct to apply deep learning algorithm on data set of
different types of cancer separately such as lung, stomach and breast cancer.

Let us exercise SWOT analysis on deep learning. Deep learning is an efficient biomedical research tool in many potential applications such as drug discovery and biomarker development. A neural network may have a single hidden layer where DNN has many hierarchical layers of nonlinear information processing units. A simple neural network does not deal well with raw data whereas deep learning can be largely unsupervised and can learn intricate patterns from even high-dimensional raw data with little guidance. Only a deep circuit can perform exponentially complex computational tasks without requiring an infinite number of nodes. DNNs can process very large high dimensional, sparse, noisy data sets with nonlinear relationships. DNNs have high generalization ability; once trained on a data set, they can be applied to other new data sets; this is a requirement for binding and interpretation of heterogeneous multiplatform data. DNNs can be classified into networks for unsupervised learning, networks for supervised learning and hybrid or semi-supervised networks.

But there are some limitations such as black box problem in quality control and interpretation of high dimensional data; selection problem in choosing appropriate DNN architecture, high computational cost of time consuming training method, overfitting problem and the need for large training data sets that may not be readily available. In case of overfitting, training error is low but the test error is high.

*Training strategy*: Data analysis on gene expression level is one of the research hotspots today. There are various types of machine learning algorithm such as k - nearest-neighbor (kNN), support vector machines (SVM), decision trees (DT), random forests (RF), and gradient boosting decision trees (GBDT). But, each machine learning method has its own flaws in terms of classification accuracy and other performance measures. It is hard for SVM to find out an appropriate kernel function. DTs have over fitting problems and RFs require more samples to attain high classification accuracy. Each machine learning algorithm may outperform others; it is rational to adopt multiple learning algorithms for better performance. There are various types of ensemble methods such as bagging, boosting, linear regression, stacking, majority voting algorithm and deep learning. Majority voting considers linear relationships among classifiers.

Deep learning has the ability to learn the intricate nonlinear structures from the
original large data sets automatically. Deep neural networks can ensemble multiple
classification models to predict cancer more accurately. To avoid over fitting, it is
required to preprocess the raw data and employ differential gene expression
analysis to select important and informative genes, which are fed to various
classification models. A deep neural network is used to ensemble the outputs of
multiple classification models to obtain the final prediction result. Deep learning
based multi-model ensemble method makes more effective use of the information of
the limited cancer data and generates more accurate prediction than single
classifiers or majority voting algorithm. There are several open issues. Is it rational
to ensemble the outputs of multiple classification models to obtain the final prediction result for same type of cancer across different demographic profile of cancer patients or various zones of the world? Is it possible to ensemble the outputs of multiple classifiers for different types of cancer?

<figure><img src="image_33.png" /><figcaption>Figure 5.1. Ensemble learning method</figcaption></figure>

Another critical issue is to improve classification accuracy in case of imbalanced, multi-class learning problems. Let us consider the case of bowel cancer data where a large number of examples of normal cases may exist with a much smaller number of positive cases of cancer. This data imbalance complicates the learning process and may result misclassification for the classes with fewer representative examples. Such uneven distribution may create a difficult learning process for finding unbiased decision boundaries. A regularized ensemble model of deep learning employs regularization that accommodates multiclass data sets, automatically determines error bound and penalizes the classifier when it misclassifies examples that were correctly classified in the previous learning phase and can attain superior performance in terms of improvement of overall accuracy for all classes.

Testing strategy: Let us consider three data sets of three kinds of cancers such as Lung cancer (LCD), Stomach cancer (SCD) and Breast cancer data (BCD).

* ROC curve for LCD: True positive rate vs. false positive rate for KNN, SVM, DT, RF, Majority voting, ensemble

* ROC curve for SCD data

* ROC curve for BCDA data

* Dataset, data size, precision (%), recall (%), accuracy(%), CPU time(S)

* Predictive accuracy (%) of various classification algorithms

**Optimal Margin Classifier for Cancer Detection**: The expert panel are trying to explore whether an efficient optimal margin classifier such as support vector machine can be applicable for detection of cancer. Here, the most important issue is the performance of SVM in terms of classification accuracy and privacy of data. Classification comprises of two subtasks: learning a classifier from training data
with class labels and predicting the class labels for unlabeled data using the learned
classifier. Data can be stored as feature vectors. In the classification problem of
detecting cancer, the training data comprises of patients with labels cancer or no
cancer. Support Vector Machine (SVM) is the most well-known kernelized
maximum margin classifier. The learning methodology is the approach of using
examples to synthesis programs for computing the desired output from a set of
inputs; it generates target decision function in hypothesis space. The learning
algorithm selects training data as input and selects a hypothesis from the hypothesis
space. There are various methods of learning such as supervised learning from
training examples, unsupervised learning, batch learning and online learning. The
next issue is generalization - how to assess the quality of an online learning
algorithm? Consistent hypothesis performs correct classification of the training
data. The ability of a learning machine to correctly classify data not existing in the
training set is known as generalization. How to improve generalization: Ockham's
Razor suggests that unnecessary complications are not helpful; it may result overfit.
Support Vector Machine (SVM) is a learning system that uses a hypothesis space of
linear functions in a high dimensional feature space, trained with a learning
algorithm from optimization theory that implements a learning bias derived from
statistical learning theory. In case of pattern classification problem, the objective of
SVM is to devise a computationally efficient way of learning for finding out an
optimal capacity of the pattern classifier to minimize the expected generalization
error on a given amount of training data by maximizing the margin between
training patterns and class boundary. The basic properties of SVM are that the
learning system is suitable for both linear and nonlinear problems, computationally
efficient algorithm through modular and dual representation in Kernel induced
high dimensional feature space and optimized generalization bounds.

Learning in Kernel induced feature space is as follows:

Target Function – f(m₁,m₂,r) = C*m₁*m₂/r²; g(x,y,z) = ln f(m₁,m₂,r) = ln C + ln m₁ + ln m₂ - 2 * ln r = c + x + y - 2z; A linear machine can learn g but not f. A kernel is a function K such that for all x,z ε X; K(x,z) = <φ(x), φ(z)> where φ is a mapping from input space X to inner product feature space F.

$$
\langle x,z \rangle^2 = (\sum_{i=1}^{n} x_i z_i)^2 = (\sum_{i=1}^{n} x_i z_i) (\sum_{j=1}^{n} x_j z_j) = \sum_{i=1}^{n} \sum_{j=1}^{n} x_i z_j x_j z_i = \sum_{i,j=(1,1)}^{n,n} (x_i x_j) (z_j z_i) = \phi(x), \phi(z)
$$

Let us consider linear classification problem. Given a linearly separable training
sample S = ((x₁, y₁), ..., (x_l, y_l)), the hyperplane (w,b) realizes the maximal margin
hyperplane with geometric margin γ = 1/ ||w||₂ by solving the optimization problem
: Minimize <w,w> Subject to yᵢ (<w.xᵢ> + b) ≥ 1 where i = 1, ..., l; In case of SVM,
the strategy is to find the maximal margin hyperplane in kernel induced feature
space. In case of linear classification problem, the equation of hyperplane is f(x) =
<w.x> + b = 0 or, f(x) = Σᵢ=₁  wᵢ xᵢ + b = 0; An input x = (x₁, ..., xₙ) belongs to positive
class if f(x) ≥ 0; An input x = (x₁, ..., xₙ) belongs to negative class if f(x) < 0. The
decision function: h(x) = sgn (f(x)) = sgn(<w.x> + b); Functional margin of an
example (xᵢ, yᵢ) is γᵢ = yᵢ (<w.xᵢ> + b). Geometric margin measures the Euclidean
distance between an example and the hyperplane, it is expressed as normalized
linear function (w/ ||w||, b/ ||w||). Updation rule of SVM algorithm in primal form is
as follows: if γᵢ or yᵢ (<w.xᵢ> + b) ≤ 0 then wₖ₊₁ ← wₖ + ηyᵢxᵢ, k ← k₊₁ γᵢ>0 indicates
correct classification of the training example. Duality is the one of the basic features of Support Vector Machine; SVMs can be modelled as linear learning machines in a dual fashion. In this case, updation rule of the perceptron algorithm is expressed in dual form. The solution i.e. final hypothesis of weight vector is a linear combination of training points. Each pattern xᵢ is associated with an embedding strength αᵢ, which is proportional to the no. of times misclassification of xᵢ causes updation of the weight.

$$ 
\begin{aligned}
w &= \sum_{i=1}^{l} \alpha_i y_i x_i \\
f(x) &= \langle w, x \rangle + b = 0
\end{aligned}
 $$

$$ 
\begin{aligned}
&= (\sum_{i=1}^{l} \alpha_i y_i x_i, x) + b \\
&= (\sum_{i=1}^{l} \alpha_i y_i \langle x_i, x \rangle + b)
\end{aligned}
 $$

In the dual form, the updation rule and decision function can be expressed in the form of inner product space $\langle x_i, x \rangle$.

Kernel Based Algorithms; Two separate learning functions; Learning Algorithm in an embedded space; Kernel function performs the embedding; Embed data to a different space; Possibly higher dimension; Linearly separable in the new space. Kernels themselves can be constructed in a modular way. Modularity is one of the important properties of SVM.

**Learning In Kernel Induced Feature Space** : Kernel methods exploit information about the inner products between data items. Kernel function can simplify the computation of separating hyperplane without explicitly carrying out the map in the feature space. The number of operations to compute the inner product space is not proportional to the number of features. Hence, high dimension of feature space does not affect the computation. Since F is high dimensional, the RHS of this equation is computationally very expensive.

$f(x) = \langle w, x \rangle + b = (\sum_{i=1}^{l} \alpha_i y_i \langle x_i, x \rangle + b)$; it is the equation of hyperplane in input space. $f(x) = \langle w, \phi(x) \rangle + b = (\sum_{i=1}^{l} \alpha_i y_i \langle \phi(x), x \rangle + b)$; it is the equation of hyperplane in feature space where $\phi$ is a mapping from input space to inner product feature space. $f(x) = \sum_{i=1}^{l} \alpha_i y_i K(x_i, x) + b$ where K = Kernel function

Next, let us consider computation of geometric margin. Geometric margin is the functional margin of a normalized weight vector. In case of linear classifier, the hyperplane function (w,b) does not change if we rescale the hyperplane (mw,mb) where m ∈ R⁺ due to inherent degree of freedom. So, to optimize the geometric margin, we can consider functional margin of 1 for two points x⁺ and x⁻. $\langle w, x \rangle + b = +1$; $\langle w, x \rangle + b = -1$; Geometric margin $\gamma = 1/2(||w||_2)$ [$\langle w, x \rangle + b - \langle w, x \rangle - b$] = $1/||w||_2$.

SVM problem formulation is as follows. Given a linearly separable training sample S = ((x₁, y₁), ..., (xᵢ, yᵢ)), the hyperplane (w,b) realizes the maximal margin hyperplane with geometric margin $\gamma = 1/||w||_2$ by solving the optimization problem - minimize $\langle w, w \rangle$ subject to $y_i (\langle w, x_i \rangle + b) \ge 1$ where i = 1,...,l; this is a convex optimization problem minimizing a quadratic function under linear inequality constraints.

**Primal Form** - $L(w, b, \alpha) = 1/2\langle w, w \rangle - \sum_{i=1}^{l} \alpha_i [y_i (\langle w, x_i \rangle + b) - 1]$; SVM problem formulation is as follows.

$$ \partial L(w, b, \alpha) / \partial w = w - \sum_{i=1}^{l} \alpha_i y_i x_i = 0; \partial L(w, b, \alpha) / \partial b = \sum_{i=1}^{l} \alpha_i y_i = 0; w = \sum_{i=1}^{l} \alpha_i y_i x_i $$
Dual Form ----- L(w, b,α) = 1/2<w.w> - Σ[αᵢ(yᵢ(<w.xᵢ> + b) - 1)] = Σαᵢ - 1/2Σαᵢαⱼyᵢyⱼ<xᵢxⱼ>

The quadratic form of SVM problem formulation is as follows. Given a linearly separable training sample S = ((x₁, y₁), ..., (xₙ, yₙ)), the hyperplane (w,b) realizes the maximal margin hyperplane with geometric margin γ = 1/||w||₂ by solving the optimization problem in dual form

Maximize Σᵢ=₁αᵢ - 1/2 Σᵢ=₁αᵢαⱼyᵢyⱼ<xᵢxⱼ>

Subject to Σᵢ=₁αᵢyᵢ = 0; αᵢ ≥ 0

w* = Σ[α*ᵢyᵢxᵢ = Desired solution; b* = 1/2 [ max yi=-1 < w*.xᵢ > + min yi=1 < w*.xᵢ > ]

Optimal hyperplane --- f(x,α*,b*) = Σ[α*ᵢyᵢ<xᵢ.x> + b*

The advantages of SVM is no local minima due to convexity of the quadratic optimization problem, it is an advantage over neural networks. The open issue is speeding up the training method, choice of appropriate Kernel functions and use of Kernel methods in other algorithms.

**Case based reasoning (CBR)** is a methodology for solving problems by utilizing previous experience. It involves retaining a memory of previous healthcare problems and their solutions and solving new problems by referencing the past cases. A healthcare expert (e.g. oncologist) presents a new query case to CBR system. The system searches its memory of past cases stored in case base and attempts to find a case that has the same problem specification of the current case. If the system does not find an identical case in its case base, it will attempt to find the case or cases that match most closely to the current query case. There are two different types of search such as similarity search and neighborhood search. In case of similarity search, the solution of the retrieved case is directly used for the current problem. The system adapts the retrieved cases if the retrieved case is not identical to the current case. In a complex search, the system requires the access of multiple case bases which are located at various locations. Let us consider a simple CBR algorithm.

CBR Mechanism

Agents: Healthcare consultant (e.g. oncologist);

Input: New case or query (q) regarding the immunity problem a patient;

Protocol:

Retrieve the most similar cases (c₁,...,cₖ), k nearest neighbors w.r.t. q from the case base;

Adapt the proposed solutions to a solution s(q), compute s(q) by combining the solutions sⱼ of the cases cⱼ. sⱼ is weighted as per the differences between cⱼ and q;

Learn after applying s(q) to q in reality; Store the new solution in the case base for solving q'.

Evaluate performance: Rejection ratio = no. of unanswered queries / total no. of queries.

Output: Recommended solution;

CBR is selected for cancer due to various reasons. The healthcare domain has an underlying model, the process is not random and the factors leading to the success
or failure of a solution can be captured in a structured way. Cases recur in healthcare domain though there may be exceptions and novel cases. Healthcare solutions can be improved through case retrieval and case adaptation. Relevant healthcare cases are available at different healthcare institutes; it is possible to obtain right data. *Case retrieval* is the process of finding within the case base those cases that are the closest to the current case. There must be criteria that determine how a case is evaluated to be appropriate for retrieval and a mechanism to control how the case base is searched. Most often, an entire case is searched. But, partial search is also possible if no full case exists.

A case is a record of a previous experience or problem in terms of problem definition, patient's symptoms, drugs, solution methodology, test results and recommendations. A case base also stores global best practices, standards, valid drugs, price and contacts of specialists. Data is stored based on domain knowledge and objectives of the reasoning system. The cases should be stored in a structured way to facilitate the retrieval of appropriate case when queried. It can be a flat or hierarchical structure. *Case indexing* assign indices to the cases for retrieval and comparisons. There are different approaches of case retrieval. In case of nearest neighbor search, the case retrieved is chosen when the weighted sum of the features that match the query case is greater than the other cases in the case base. A case that matches the query case on n number of features is retrieved rather than a case which matches on k number of features where k < n; different features may be assigned with different weights. Inductive approach is driven by a reduced search space and requires reduced search time. This results reduced search time for the queries. Knowledge based approaches select an optimal set of features of case by using domain knowledge. The complexity of case retrieval depends on multiple factors: (a) number of cases to be searched, (b) domain knowledge, (c) estimation of the weights for different features and (d) case indexing strategy.

*Case adaptation* is the process of translating the retrieved solution appropriate for the current problem; it adds intelligence to the recommendation process. There are various approaches of case adaptation. The retrieved case can be directly used as a solution to the current problem without any modification. Otherwise, the retrieved solution should be modified according to the current problem. The steps or processes of the previous solution can be reused or modified. The solution of the current case can be derived by combining knowledge of multiple retrieved cases. Case adaptation is a complex decision making task, it considers multiple factors: how close is the retrieved case to the query case? How many parameters are different between the retrieved and the query case? DMAs can apply common sense or a set of rules or heuristics for case adaptation.

Making sense of the information found during an investigational web search is a complex task of case based reasoning. *Sense making* is to find meaning in a situation; it is the cognitive act of understanding information. The system should support collaborative information search by providing several rich and interactive views of the search activities of a group. One of the problems is the design of computer interfaces to enable sense making of the processed information. Sense making is not only important for individuals, but also for groups to achieve shared goals. Traditional sense making tools focus on data mining, provide better
information representation, visualization and organization of search results. But, it
is also required to support the collaboration and communication that occurs among
the investigators when they make sense of information together.

Dr, Jim Morrison and Dr. Arvind Gurumurthy are presenting on biomedical system for health security. It is essential to analyze in terms of state, complexity, feedback loop, physical and information flows. The basic objectives of system analytics are to analyze complex and dynamic interactions among various components of different types of biomedical devices such as artificial pancreas, liver, kidney, cardiovascular devices and limbs.

*Artificial pancreas*: An artificial pancreas is expected to perform various types of functions such as continuous blood glucose monitoring without manual intervention of the user, monitoring trends of rising and falling blood sugars for the prediction of blood glucose levels in the immediate future, comparing blood sugar levels against a high threshold, and prompting for a correction bolus from the insulin pump and also comparing blood sugar levels against a low threshold and prompting to reduce the basal insulin from the pump. A stream of real-time data is used for close loop control of the insulin pump. The critical components of artificial pancreas are sensors, control algorithm and insulin pump.

*Artificial liver*: Next, let us consider the innovation of artificial liver. Liver failure can be overcome by orthotropic liver transplantation and this motivates the development of various artificial and bioartificial liver support devices [33-35, 36]. Artificial systems are based on the principles of adsorption and filtration; bioartificial devices are based on the provision of liver cells. Such artificial livers support detoxification, synthetic and regulative functions. In case of orthotropic liver transplantation, many patients may not survive until a suitable donor organ is available since donor organs are rare. Contraindications do not permit liver transplantation. For these problems, artificial devices are essential to bridge the patient to transplantation or temporarily support the failing organ. Cell free artificial systems use adsorption and filtration through removal of toxins from the patient's plasma. Haemodialysis is used for treatment of liver failure to remove water soluble toxins.

A bioartificial liver device (BAL) is an artificial supportive device based on bioengineering for acute liver failure patient. Is it really feasible to develop a complex artificial liver which can mimic each function of normal and healthy liver? Dr. Kenneth Matsumara developed BAL based on liver cells obtained from an animal; a semipermeable membrane allows toxins and blood proteins to pass. Advancements in bioengineering techniques have led to improved membranes and hepatocyte from various cell sources such as primary porcine hepatocytes, primary human hepatocytes, human hepatoblastoma (C3A), immortalized human cell lines and stem cells. But, BAL can not replace liver functions permanently and serve as a supportive device for acute liver failure.

There are several limitations of bioartificial liver support. In most cases, the liver cells are separated from the patient's blood or plasma by at least one membrane which provides an immunological barrier limits the exchange of substances and reduces the effectiveness of the system. The blood/plasma flow is limited to 100–300
mL/min whereas the blood flow in a normal human liver is about 1500 mL/min. Then, what should be rational risk mitigation strategies for the problems of liver? In charcoal haemoperfusion, there is risk of biocompatibility, loss of thrombocytes and clotting problems. Plasma exchange using filters requires a large plasma stock and bears the risk of infections. But, improved biochemical and clinical conditions and removal of toxins may not ensure survival benefit for the patients. It is essential to explore more sophisticated detoxification systems like albumin dialysis, fractionated plasma separation, continuous albumin purification system (CAPS) and Single Pass Albumin Dialysis (SPAD). Bioartificial systems give support in synthetic and regulatory function of the liver besides detoxifying the patient's plasma. Primary human cells meet the demand of biocompatibility. But, there is risk of infections and metastasis formation; the metabolic compatibility is not assured. It is really hard to find ideal cell source.

**Artificial kidney** : Let us now consider the technological innovation of artificial kidney. The system needs focus on several areas such as maturation of hemodialysis and hemofiltration therapy, improvements in materials and hemodynamic areas, biocompatible materials, superior transport methods for toxin removal, bioartificial renal tubule, homeostatic functions, selective toxin removal without concomitant elimination of beneficial proteins, absorption removal pathway (with affinity methods to eliminate uremic toxins), tissue engineering and improved patient management techniques [18-24]. An artificial kidney is expected to perform a number of important metabolic, endocrine and active transport functions of living kidney.

**Artificial cardiovascular devices**: The basic objectives and challenges of the design of artificial cardiovascular devices is to replace various lost functions of heart, improve the performance of artificial valves, stents and pacemakers and to minimize the side effects and risks of complex surgical procedures. The number of surgical operations of valve replacement, stents and pacemakers has been increasing day-by-day due to various reasons like congenital valve defects and acquired valve disease of various patient groups and valve location. Valve replacement can be done for aortic or mitral valves. The design of artificial valves is evolving based on minimally invasive valve implantation and valvular tissue engineering through the advancement of computational fluid dynamics and computationally intensive simulation modeling techniques. Simulations can predict the performance of both bioprosthetic and mechanical valves and analysis of various complications such as structural failure, thromboembolism, hemolysis, paravalvular regurgitation and endocarditis.

Stent is used to hold tissue in place or provide a support for a graft and applicable to the diseases of peripheral and coronary arteries. Stents may be of two types such as balloon-expandable and self-expanding. The design of stent is evolving in terms of advanced material science [metal, alloys (e.g. tantalum, steel, Nitinol), biodegradable and nondegradable polymeric stents], improvement of system performance, reduction of the risks of restenosis, ease of handling and stable long term system performance, simple and effective deployment method. Percutaneous Transluminal Coronary Angioplasty (PTCA) moves a catheter mounted balloon to specific site and inflated to displace the tissue and create a wider lumen in the blood vessel. Percutaneous coronary intervention (PCI) refers to a set of procedures like
PTCA, atherectomy, thrombolysis, intravascular radiation and correct placement of stents.

The technology of pacemakers and ICDs (cardioverter defibrillators) is evolving in terms of dual chamber activity, cost reduction, potential harmful interactions, reduction in number of leads, improved device function, advances in technology and implantation techniques. A pacemaker delivers an electrical impulse to depolarize the heart chamber in a spreading and coordinated way like a normal heartbeat. In contrast, defibrillators are used to depolarize the entire heart at once to terminate uncoordinated contractions. It is extremely useful while electric impulse conduction or initiation in the heart is blocked, slowed or triggered irregularly. The technology of artificial vascular grafts is passing through technological and clinical advancements such as endovascular therapies (angioplasty, stent), less invasive interventions for vascular repair and reconstruction, reduction in open excision and replacement, development of tissue engineered blood vessels (TEBV), performance improvement of vascular graft designs, novel coatings, improved biocompatibility and minimization of hematologic alterations. The other critical technologies are circulatory support devices for chronic congestive heart failure, artificial lungs, intra-aortic balloon pump (IABP), ventricular assist devices (VADs) and total artificial hearts (TAH). The technology is evolving with the advancement of electronics and software engineering. IABP requires that a patient maintains some native pumping capacity. A cardiac transplant is the last resort for critical patient care which fails conventional medical and surgical treatment. But, the supply of donor organs is limited today.

*Artificial limbs*: Is it possible to design artificial limbs from the concept of robotic arms? What are the similarities and differences? What are the mechanical constraints: size, weight and power? What are the other design constraints: interface between artificial limbs and human body, sensors, strong light weight materials for prosthetic socket and interfaces; how to mimic the functions of biological limbs of human beings from the perspectives of ease of use, comfort and flexibility? what should be the system control mechanisms? Is it possible to design artificial limbs through CAD / CAM software? An intelligent system analytics is expected to resolve these issues accurately.

*Case analysis for biomedical technology innovation*: Let us consider three emerging technologies based on biomedical instrumentation, computer science and AI: (a) Laser therapy, (b) Wearable & Pervasive computing and (c) Surgical robots. These technologies may be interesting and good solutions for cancer care in future.

*Laser therapy for cancer care*: This section analyzes the emerging trend of laser technology for various biomedical applications such as dental problems, piles, fissures and fistula [63]. In previous session, we have already shown the application of laser for cancer care alongwith details of various advantages. In case of dental problems, smart diode laser is effectively used for the treatment of both hard and soft tissues with utmost care, precision, consistency, speed and control. The diode laser is a solid state semiconductor which produces laser wavelength (e.g. 980 nm). Laser therapy is minimally invasive, less painful, takes shorter time, reduces
number of seating and ensures better healing and changes the experiences of the
dental patients positively.

This laser system uses two wavelengths of 635 nm and 980 nm through transmission or absorption effects. 635 nm shows excellent photo biological effects due to high transmission in water. It is also optimally selected for photochemical effects and local ozone therapy activated by tolonium chloride. 980nm wavelength shows high soft tissue absorption efficiency and excellent safe surgical results for soft tissue cutting at low power radiation. Smart diode laser is expected to be cost effective (e.g. low operating cost), light weight, easy to handle, supporting preset procedures and customized programmes; equipped with large touch screen and LCD display, high storage space and advanced security features. 980 nm 10W is used for *soft tissue cutting* (e.g. coagulation, gum contouring and reshaping, gingive retraction, Haemostasis in gingival sulcus for prosthodontics impression, exposure of implants, lingual and fabial frenotoma and frenectomy, abscess opening, Epulis papiloma and fibroma removal periodontology, elimination of bacteria in periodontal pockets endodontic, sterilization of root canal and closure of microchannels); *aesthetic dentistry* (e.g. power bleaching of viral and non viral teeth) and *biostimulation* (e.g. wound healing, root canals, support in treatment of periapical lesions, periodontitis and periimplantitis, decontamination of cavities before filing treatment of chronic and recurrent aphthae, hygienisation of cervical area after scaling) and photoactivated disinfection (e.g. : root canals, periodontal pockets, support in the treatment of periapical lesions, periodontitis and periimplantitis, decontamination of cavities before filing treatment of chronic and recurrent aphithae, widespread and local inflammation of the mouth caused by herpes virus).

Diode laser therapy offers various types of benefits such as less invasive, less painful, less bleeding, less trauma, faster healing, no scars, less time (e.g. not more than an hour) and can be treated in an ambulatory condition with local anesthesia. It provides the choice of two specific wavelengths: 980nm, 10/15 W for haemorroids and fistula as safe and efficient absorption coefficient and 1470nm, 15W for varicose vein and 2 types of fibres with open end or radial. It is a cutting edge technology; has low operating cost, very compact and small sized device and may have extendable database of predefined therapy protocols and flexible customized parameters. In case of fistula, laser energy is delivered via radial fibre into the anal fistula tract and is used to thermally ablate and close off the abnormal pathway. It gives good control to the operator in surgical operation and ensures fast healing process. The tissue is destroyed and the fistula tract collapses to a very high degree. So, laser therapy can be effectively used for colone cancer care.

**Pervasive and wearable computing for cancer care** : One of the most promising emerging digital technology is health monitoring smart wearable systems (SWS) through advances of microelectromechanical systems, electrical simulation, mechatronics, sensors, actuators, biomedical instrumentation and nanotechnology. SWS is an interesting cost-effective solution which can monitor a patient's health status in real-time and support complex healthcare applications for disease prevention, symptom detection and medical diagnosis. Let us consider the structure of smart wearable system (SWS). The system may have various types of digital and mechatronics components such as sensors, actuators, power supplies, wireless
communication and processing units, algorithms, software, UI and smart fabrics to
capture and process data and make intelligent decisions based on the measurement
of various parameters of human body such as temperature, blood pressure, heart
rate, respiration rate, blood oxygen saturation, EEG and ECG. The measured data
are sent to a central node (e.g. PDA, medical centre) through wireless
communication system. SWS is expected to monitor the state of the health of human
agents (e.g. patients, athletes), issue alerts and send feedback to the medical staff in
real-time. The healthcare experts and consultants can take rational decisions on
patient care accordingly. There are various issues and challenges in wearable and
pervasive computing, telecare, tele-health and telemedicine through new models,
prototypes, test beds and industrial products to enhance the performance of
healthcare system and minimize the risk of illness, injury, inconvenience and
rehabilitation. There are some constraints in terms of high cost, size, weight, energy
consumption, complexity of sensor implementation and connectivity, ethics, laws,
information security and privacy, freedom, autonomy, reliability, consistency, safety
and service issues.

*Surgical robotics for cancer care:* Artificial intelligence simulates human intelligence and develops algorithms that can learn and perform intelligent behavior with minimal human intervention, high precision, accuracy and speed. Robotics is an interdisciplinary branch of mechanical, electrical and electronics engineering and computer science. Various domains of AI are used in medical robotics such as computer vision, edge computing, deep, transfer and reinforcement learning. A medical robot is a programmed machine designed to execute one or more tasks automatically and repeatedly with speed and precision for delicate and complicated surgical operations where human skills may not be applicable appropriately. The basic objectives of medical robotics are design, construction, operation and use of robots and related information system for control, feedback and information processing and development of sensors and human robot interface. The critical components of a robot are controller or brain run by a computer program; robotic operating system; electrical parts such as motors, sensors for sensing and touch, power sources: batteries, solar power; mechanical parts such as actuators, effectors, grippers, manipulators, locomotion devices, air muscles, muscle wire, pistons, grippers, wheels, and gears that make the robot move, grab, turn and lift.

SWOT analysis on various surgical methods: Robotic surgery allows surgeons to perform complex, delicate and minimally invasive surgical tasks with more precision, flexibility and control as compared to conventional methods through tiny incisions using robotic technology. Surgical robots (e.g. da Vinci Surgical System) are self powered, computer controlled devices that can be programmed to aid in the positioning and manipulation of surgical instruments. The system includes a camera arm and mechanical arms with surgical instruments. The surgeon controls the arms while seated at a computer console near the operating table. The console gives the surgeon a high definition and magnified 3-D view of the surgical site. The surgeon leads other assisting team members. There are various types of benefits as compared to conventional open surgery such as enhanced precision, flexibility and control, transparency in view, minimally invasive prostate and heart surgery, safe, fewer complications (e.g. surgical site infection, less pain and blood loss, quicker
recovery, smaller, less noticeable scars). Medical robotics is being widely used in USA and Europe. But, there are several constraints such as skill, knowledge and cost. A single robot may cost $2'. There may be risk of death in critical cases. Robotic surgery may cost $3,000 - $6,000 more than traditional laparoscopic surgery. Robotic surgery offers a greater range of motion and precision, less bleeding and post operative pain as compared to laparoscopic surgery. Is it really possible to replace surgeons by robots in future?

Dr. Nissim and Dr. Harvey are trying to solve a puzzle. The Global Health Forum Organization is expected to redefine and implement global healthcare policy for improved immunity : "Healthcare for all at reasonable cost and optimal quality of service maintaining rationality, fairness, correctness, transparency, accountability, trust, reliability, consistency, commitment and safety through collaborative and collective intelligence". How to code the program for sustainable physical, mental and social health globally? It is rational to adopt a set of intelligent information and communication technologies for epidemic and pandemic outbreak control:

* Artificial immune mechanism

* Intelligent broadcast communication protocol

* Intelligent analytics : Real-time online data tracking system, Data mining system (e.g. Bio-informatics)

* E-governance system : Online grievance or complaint registration and tracking system for municipal corporation and gram panchayet

**Artificial Immune System** : Human immune system is an adaptive, robust, complex and distributed information processing system which protects the health of the biological system from the attacks of malicious foreign pathogens (e.g. virus, bacteria, fungi, protozoa, parasitic worms). It discriminates self from non-self elements. The immunity is either innate or adaptive; innate immunity detects and kills specific known invading organisms; adaptive immunity, responds to previously unknown foreign organisms. The challenge is how to balance innate and adaptive immunity effectively. Let us try to develop Artificial Immune System.

## Artificial Immune Mechanism

**System** : Biological system of human agents; life-science supply chain, healthcare service chain;

**Input** : A self-set S ⊆ U, a monitoring set M ⊆ U for a given system parameters;

**Output**: for each element m ∈ M, either self or non-self / danger or normal;

D ← set of detectors that do not match any s ∈ S.

for each m ∈ M do

{ 
  call threat analytics (A_h) → sense danger signal;
  secure function evaluation → verify innate and adaptive system immunity
  i = f(a_h,b_h,c_h,d_h,e_h);
}

sense-challenge-respond to system immunity resiliently;
if m matches any detector $d \in D$ then identify m as non-self;
else identify m as self;
check if non-self or suspicious element is benign or malign danger;
if it is malign then quarantine and suppress it else give alert for social distancing;.

Moves :

⊕ Define *system immunity* ($S_i$) in terms of innate or natural immunity and adaptive immunity (humoral and cell mediated)

⊕ *Sense danger signal*

⊕ *Self-nonself classification*

⊕ *Clonal selection*

• *Hotspot*

• *Containment area*

• *Cluster configuration*: red, orange and green zone

⊕ Negative selection, somatic hypermutation and suppression

⊕ Multidimensional view of *intelligent reasoning* (perception, common sense, logical, analytical, forward and backward chaining, sequential, parallel, uncertainty, probabilistic, approximation, predictive, imaginative)

⊕ *Case based reasoning*, standardization and benchmarking of medical practice

⊕ *Testing* (PCR,PPCR,RAT)

⊕ Private search for evidence

Complexity Analysis of AIM : Artificial intelligence (AI) is basically simulation of human intelligence. Recently, there are debates on AI. Let us consider the outlook given by Prof. Nick Bostrom in his book ‘Superintelligence : Paths, Dangers, Strategies’ : Today’s world need to be super careful with AI because it is potentially more dangerous than nukes. Is nuclear power really safe for our society as compared with solar power? Can we forget the tragedies of Hiroshima, Nagasaki, Chernobyl and Fukushima disaster? Is it really true or an instance of rational reasoning; can we forget the contributions of artificial intelligence in engineering, medical and management science so easily? Let us look at another view of Prof. Don Perlis of University of Maryland, College Park, USA in his work ‘Five dimensions of reasoning in the wild’ : an intelligent reasoning system demands new data structure beyond knowledge base with envision, perception and proper assessment of a problem; reasoning is not effective when done in isolation from its significance in terms of the needs and interests of an agent with respect to the wider world. A rational reasoning system needs the support of an intelligent analytics. AI needs a balanced and holistic approach from the perspectives of collective, collaborative, machine, business and security intelligence not only in engineering technology and management science but also medical science, efficient policy formulation and iteration for global healthcare and family welfare progrmammes.

Now, let us define the problem of the present work. The basic objective is to evaluate the natural and adaptive immunity of a complex system. It is not rational to mix the concept of immunity with security. Security is an essential part of immunity; the evaluation of immunity needs the reasoning on the needs and interest of the complex system with a broader outlook. The evaluation of the immunity of a
system involves modeling, defining complete specifications and verification. First, it
is essential to develop the model (m) of a system by proper representation of its
various states and programs. Next, it is important to specify the properties (p) of the
system through logical reasoning. Finally, it is essential to develop a verification
mechanism which justifies: does the model (m) satisfies the properties (p) indicating
a healthy immune system? The evaluation of immunity of a system can be done by
exhaustive search of the state space (local, global, initial and goal states and state
transition relations) of a system through simulation, testing, deductive reasoning,
model checking and intelligent search. The procedure terminates with positive or
negative answer; the positive answer indicates a healthy immune system; the
negative results provide an error trace indicating incorrect modeling or
specification of the system or the occurrence of malicious threats.

***Critical observation:*** A biological system ensures optimal level of immunity by balancing natural and artificial intelligence based on intelligent reasoning. A human agent must have common sense healthcare knowledge base for proper biological system control through intelligent self-assessment and self-confidence i.e. how to manage self against non-self. It demands the necessity of learning the basic concept of ‘immune system’ through artificial intelligence. It is possible to redefine global healthcare policy based on AIM-HS mechanism. Let us analyze the AI moves for this mechanism through a set of puzzles.

***Move 1:*** Multidimensional view of intelligent reasoning should be based on rational selection of single or multiple techniques from the list of logical, analytical, case based, forward and backward chaining, sequential, parallel, uncertainty, probabilistic, approximation, predictive, imaginative and perception based reasoning. AI reasoning is a consortium of methodologies that works synergistically and provides flexible information processing capability for handling ambiguous situations in healthcare domain. The basic objective is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in tractable, robust and low cost solutions. Let us review the scope of these reasoning techniques to ensure health security.

***Puzzle:*** *Can perception be an effective reasoning technique for improved immunity?*

A doctor can use both complex perception based fuzzy information and simple
measurement based crisp information for intelligent decision making in patient
care. Even, a patient can take critical healthcare decisions in time based on
perception. But the hard question is how to evaluate the positive or negative impact
perception on the immunity of a biological system? Human agents can perform
different types of physical and mental tasks without any measurements and any
computations based on common sense reasoning or heuristics. Heuristics are
intuitive knowledge or thumb rules learned from experience. In healthcare domain,
a doctor can understand the medical problems of the patients and recognize
symptoms, similarities and dissimilarities through the perception of time, distance,
force, direction, shape, color, odor, taste, number, possibilities, likelihood, truth and
other different types of attributes of physical and mental objects. Perception is the
basic building block of approximate reasoning. Recognition and perception are closely associated. Recognition is a sequence of decisions, decision are made based on information and the information is a mix of measurements and perceptions. Measurements are crisp (e.g. Body weight is 60 kg.) while perceptions are fuzzy (e.g. body weight is normal). Perception may be converted into measurements but such conversions may be counterproductive, unrealistic and infeasible. Alternatively, perceptions are converted into propositions expressed in natural languages such as a patient is very weak. Can relaxation music, yoga and meditation be a good solution for the treatment of mental health though the entertainment world is getting flooded with boom boom digital dhamaka!

Robots are increasingly used for complex surgical operations such as brains, eyes, hearts and hip replacements. Intelligent robotic walkers and toys are used for elderly and handicapped people. Robots are equipped with sensors for perceiving their environment and effectors with which they can assert physical forces on their environment. Perception is the process by which robots map sensor measurements into internal representations of the environment. Perception is a complex process as the sensors are noisy and the environment is partially observable, unpredictable and dynamic. Robots have e problems of state estimation or filtering. Good internal representations imply that robots have sufficient information to make good decisions, they are structured and updated efficiently and they are natural. Machine learning plays an important role in robot perception.

*Puzzle : Can you explore the impact of forward chaining, backward chaining, uncertainty, probabilistic and imaginative reasoning on the immunity of a biological system?*

The effectiveness of reasoning depends on various factors : knowledge base, sound and complete logic, inference rules, intelligence and decision making capability of agents and knowledge representation technique. In complex decisions, the expert knowledge may not be expressed in terms of single rules but in the form of chaining multiple rules together based on available data. There are two approaches of inferencing such as forward and backward chaining. Forward chaining views IF part of a rule first and the rule makes conclusion when all IF conditions are met. Backward chaining is the reverse of forward chaining. It starts from the conclusion and then identifies the IF conditions. A human agent can assess the risks on the immunity of the biological system through learning from examples i.e. supervised learning, unsupervised learning, forward chaining, backward chaining, uncertainty, probabilistic and approximate reasoning. Probabilistic reasoning evaluates uncertainty which may be inescapable in complex, nondeterministic or partially observable environments. Probabilities indicate inability of an agent to reach a definite decision; summarize the agent's beliefs with respect to the evidence. It is important to combine the agent's beliefs and desires and fix the best action plan that can improve expected utility. Let us consider a hypothetical case on envisioning i.e. imagination or anticipation of alternatives. A kid was treated with antibiotics as per treatment plan A (say allopathic). The kid was affected with chronic skin allergic disorder due to the side effects of antibiotics. His parents took the decision to shift
from treatment plan A to plan B (say homeopathic). The kid recovered from the allergic disorder. After one year, the kid was affected with diphtheria, pneumonia and chicken pox sequentially. He was shifted to treatment plan C (allopath). He was injected with a serum of bactriofuz virus which was used to kill bacillus diphtheri bacteria. Later, he had taken injections for three consecutive years to resist the effect of bactriofuz virus. The kid was recovered from all these diseases successfully. This case shows clearly how intelligent reasoning is important for correct decision making against immunological problems.

**Puzzle:** Can case based reasoning be an efficient technique for improved immunity?

Case based reasoning (CBR) is a methodology for solving problems by utilizing previous experience. It involves retaining a memory of previous healthcare problems and their solutions and solving new problems by referencing the past cases. A healthcare expert presents a new query case to CBR system. The system searches its memory of past cases stored in case base and attempts to find a case that has the same problem specification of the current case. If the system does not find an identical case in its case base, it will attempt to find the case or cases that match most closely to the current query case. There are two different types of search such as similarity search and neighborhood search. In case of similarity search, the solution of the retrieved case is directly used for the current problem. The system adapts the retrieved cases if the retrieved case is not identical to the current case. In a complex search, the system requires the access of multiple case bases which are located at various locations. Let us consider a simple CBR protocol.

CBR is selected for healthcare information system due to various reasons. The healthcare domain has an underlying model, the process is not random and the factors leading to the success or failure of a solution can be captured in a structured way. Cases recur in healthcare domain though there may be exceptions and novel cases. Healthcare solutions can be improved through case retrieval and case adaptation. Relevant healthcare cases are available at different healthcare institutes; it is possible to obtain right data. Case retrieval is the process of finding within the case base those cases that are the closest to the current case. There must be criteria that determine how a case is evaluated to be appropriate for retrieval and a mechanism to control how the case base is searched. Most often, an entire case is searched. But, partial search is also possible if no full case exists.

A case is a record of a previous experience or problem in terms of problem definition, patient's symptoms, drugs, solution methodology, test results and recommendations. A case base also stores global best practices, standards, valid drugs, price and contacts of specialists. Data is stored based on domain knowledge and objectives of the reasoning system. The cases should be stored in a structured way to facilitate the retrieval of appropriate case when queried. It can be a flat or hierarchical structure. Case indexing assign indices to the cases for retrieval and comparisons. There are different approaches of case retrieval. In case of nearest neighbor search, the case retrieved is chosen when the weighted sum of the features that match the query case is greater than the other cases in the case base. A case that matches the query case on n number of features is retrieved rather than a case
which matches on k number of features where k < n; different features may be
assigned with different weights. Inductive approach is driven by a reduced search
space and requires reduced search time. This results reduced search time for the
queries. Knowledge based approaches select an optimal set of features of case by
using domain knowledge. The complexity of case retrieval depends on multiple
factors: (a) number of cases to be searched, (b) domain knowledge, (c) estimation of
the weights for different features and (d) case indexing strategy.

*Case adaptation* is the process of translating the retrieved solution appropriate for the current problem; it adds intelligence to the recommendation process. There are various approaches of case adaptation. The retrieved case can be directly used as a solution to the current problem without any modification. Otherwise, the retrieved solution should be modified according to the current problem. The steps or processes of the previous solution can be reused or modified. The solution of the current case can be derived by combining knowledge of multiple retrieved cases. Case adaptation is a complex decision making task, it considers multiple factors: how close is the retrieved case to the query case? How many parameters are different between the retrieved and the query case? DMAs can apply common sense or a set of rules or heuristics for case adaptation.

Making sense of the information found during an investigational web search is a
complex task of case based reasoning. *Sense making* is to find meaning in a
situation; it is the cognitive act of understanding information. The system should
support collaborative information search by providing several rich and interactive
views of the search activities of a group. One of the problems facing HCI research
today is the design of computer interfaces to enable sense making of the processed
information. Sense making is not only important for individuals, but also for groups
to achieve shared goals. Traditional sense making tools focus on data mining,
provide better information representation, visualization and organization of search
results. But, it is also required to support the collaboration and communication that
occurs among the investigators when they make sense of information together.

*Move 2*: Define system immunity ($S_i$) in terms of innate or natural immunity and adaptive immunity (humoral and cell mediated); sense negative selection, danger signal detection, clonal selection, somatic hypermutation and suppression;

*Move 3*: Define collective intelligence in terms of system dynamics (scope, input, output, feedback, process, agents), local and global healthcare policy, immunization programmes, benchmarked and standardized medical practice and education, common sense healthcare and hygiene (system control, self confidence: how to manage self? self assessment and monitoring, family planning), resource planning, efficiency of life-science supply chain and healthcare service chain;

AIM-HS can explore collective intelligence in terms of proper coordination and integration among system, security, strategy, structure, staff, skill, style, shared vision, service and social networking. It is a hard problem. It requires proper surveillance of life-science supply chain and healthcare service chain. The basic objective is to ensure that different organizational elements of the healthcare service provider are appropriately synchronized and work in harmony to improve the
quality of service. The hard elements are strategy, organizational structure and system. The soft elements are leadership style, resources, skill, security and shared values. The hard elements can be identified and defined in a relative simple way and directly controlled by corporate management. The soft elements are difficult to define, less tangible and influenced by corporate culture. The strategic fit among these factors is the basic building block of the effectiveness of a complete and sound global healthcare policy. A system acts rationally against bio-terrorism when these ten elements are aligned correctly and mutually reinforcing.

**Move 4** : Define machine intelligence in terms of (preconditions, post conditions, triggering events, main flow, sub flow, alternate flow, exception flow, computational intelligence, accuracy, communication cost, traffic congestion, time and space complexity, resources, capacity utilization, load, initial and goal states, local and global states) for bio-medical instrumentation and digital transformation, artificial organ transplantation (brain, heart, blood, neural system, stomach, liver, kidney, pancreas, limbs, bone marrows), wearable computing, pervasive computing and biosensors, nano-medicine and surgical robotics;

**Puzzle** : Can you assess and mitigate the risks of artificial organ transplantation on the immunity of a biological system ?

Let us first imagine of artificial blood or blood surrogate or blood extender which can mimic and fulfill the function of natural blood. Still today, it is hard to synthesize natural blood with its oxygen carrying capability due to its complex composition. Imaginative AI reasoning envisions perfluorocarbons (e.g. perfluorodecalin) as blood substitutes. But, is it possible to assess the threats of blood substitutes on the immunity of the biological system in terms of toxicity, blood group matching, malicious biological activities, retention time in the body and dissolving capability of gases like oxygen and carbon dioxide?

Next, let us consider the risks of transplantation of natural organs on the immunity of the biological system. Organ donation scheme saves life by replacing diseased organs with the healthy ones. At least seven live may be saved if a person is able to donate organ. Some organs may not be suitable for transplant such as heart, kidneys, liver, lungs, pancreas and small intestine. Such life saving organs can be only donated in case of brain death whereas tissues can be donated after cardiac death. But, can you imagine the risk of organ donation scheme from the angle of immunity? Organs can be transplanted from one person or an animal (e.g. dog, monkey, donkey, pig, goat, cow, horse and buffalo) to the sick patient. But, there is high risk of immunity problems due to transmission of deadly diseases (e.g. HIV, blood cancer). But, the problem of blindness can be resolved through transplantation of eyes; the same may be a good solution for the disorder of kidney. The society needs advanced and cost effective eye bank, blood bank and public awareness programmes of organ donation to solve critical illness.

AI has imagined the product concepts of highly complicated and costly artificial human organs such as artificial brain, heart, lungs, stomach, kidney, liver, pancreas, intestine, limbs, neural system, blood and cells though there are
technological limitations and financial constraints of biomedical electronics, instrumentation, mechatronics, robotics, computational intelligence and materials science and also critical immunity issues. It is hard to develop and simulate artificial organs due to the inherent complexities of structure, material and mechanisms of human organs. There are various risks of adaptive immunity and transmission of deadly diseases in artificial organ transplantation, artificial reproduction, common immunization programmes and breast milk feeding to the infants. AIM may be able to overcome some of those practically feasible constraints of the aforesaid imaginative reasoning through intelligent analytics and reasoning. AI community needs a new broad outlook, imagination and dreams to solve a complex problem through a set of simple mechanisms. Is it possible to evaluate the impact of artificial organ transplantation on the immunity of biological system correctly? Is it possible to synthesize artificial enzyme and hormones for the treatment of pancreatic or diabetic disorder?

*Move 5 : Define collaborative intelligence and assess its impact on the immunity of biological system in terms of information sharing principle, negotiation protocol and planning, intelligent broadcast protocol, organ donation and breast-milk feeding for childcare*

*Puzzle : Can you assess and mitigate the risks in breast milk feeding by the mothers on the immunity of their children?*

The simple act of breast feeding a new born is the first step towards a healthy human being and has a huge impact on the health of an individual throughout his or her life-time. As per UNICEF, it provides them with essential nutrients, antibodies and protection from diseases. An intelligent imaginative reasoning can explore the risk of breastfeeding for IVF mothers, HIV affected mothers, working women and special cases due to the hormonal deficiency and problem in growth and development of breast organs due to life-style, physical and mental stress, malnutrition, drug addiction, alcoholism and other various factors. Some weak babies can not suck breast milk due to disorder of tongue. Can you imagine any alternative solution or product concept mapping of breast milk feeding to the infant? Privacy and trust management is a critical issue for artificial reproduction: how to store sperm and ovum correctly and fairly for test tube baby! There may be risk of incorrect swap. The worst case may be resolved through adoption of kids. Life is full of problems; but one can explore alternative solutions through intelligent reasoning.

## 4. STRUCTURE

*Structure analytics*

*Agents : System analysts, scientists, engineers;*

*Moves: Design and configure*

* Organization structure

* Technology and medical science forums

* National level : Government, NGOs, research organizations, ;
* International level : strategic alliance among global organizations (nations, health, child);

* System architecture : Innovate a set of emerging technologies as per the goals of healthcare security;

* Level 1: Biomedical, biotechnology, pharmacy, pharmaceuticals, information technology, electrical, electronics, chemical, mechanical and civil engineering;

* Level 2: Identify fundamental building blocks of information technology - Computing schema, Data schema : database, big data analytics; Networking schema : web connectivity; security schema, application schema.

<figure><img src="image_34.png" /><figcaption>Figure 5.2: Deep learning architecture</figcaption></figure>

Dr, Jim Morrison, Prof. Michel Bolton and Prof. David Nissim are analyzing the structure of the systems for health security. The first issue is *DNN System Architecture*: There are several classes of deep learning architectures used for biological data analysis. A convolution neural network (CNN) has a deep architecture with several convolutional and sub-sampling layers. Stacked auto-encoder consists of multiple sparse autoencoders. A deep belief network (DBN) freezes the weights of previous layers and feed the output to the next layer. Restricted Boltzmann machine includes a visible layer and a layer of hidden units. Mammography is a method to screen breast cancer. Breast masses are often misdiagnosed due to variability in mass appearance and low signal-to-noise ratio. Convolutional Neural Networks can be an interesting option to classify breast masses in mammograms as benign or malignant using transfer learning, and efficient data processing strategies.

Deep learning represents a class of machine learning techniques that exploit many layers of non-linear information processing for supervised or unsupervised feature extraction and transformation, pattern recognition (e.g. classification). It is used for learning multiple levels of representation to model complex relationships among data. Higher level features and concepts are defined in terms of lower level ones and such a hierarchy of features is known as deep architecture. Deep learning is based on learning representations. An observation such as an image can be represented in many ways like a vector of pixels, but some representations make it easier to learn
from examples. Deep learning is a set of algorithms in machine learning to learn in multiple levels and at different levels of abstraction. It typically uses artificial neural networks such as multi-layer feedforward neural network and convolutional neural network.

There are three classes of deep learning architectures and techniques: (a) Deep networks for unsupervised or generative learning, (b) Deep networks for supervised learning and (c) hybrid deep networks. Unsupervised learning is used to capture high order correlation of the visible data when no information about target class labels is available. In case of supervised learning, target label data are always available in direct or indirect forms. Hybrid deep networks use both unsupervised and supervised learning techniques. Many machine learning techniques use shallow structured architectures consisting of at most one or two layers. Shallow architectures are effective in solving simple problems are not effective for complicated applications due to limited modeling and representational power. Human information processing mechanisms needs deep architectures for extracting complex structure and building internal representation from rich sensory inputs. The basic concept of deep learning comes from the domains of ANN, AI, graphical modeling, optimization, pattern recognition and signal processing. Deep learning has several advantages as compared to shallow architecture: increased chip processing abilities, significantly increased size of training data and recent advances in machine learning research have enabled the deep learning methods to exploit complex and nonlinear functions, to learn distributed and hierarchical feature representations and effective use of both labeled and unlabeled data.

Deep Learning is basically credit assignment in adaptive systems with long chains of causal links between actions and consequences. It is accurately assigning credit across many stages. A standard neural network consists of many simple connected processors or units each producing a sequence of real valued activations. Input units get activated through sensors perceiving the environment, other units through connections with weights from previously active units. Learning or credit assignment is to find weights that make the neural network exhibit desired behavior. A complex problem may require long causal chains of computational stages. Convolution Neural Networks (CNN) architecture are widely used for computer vision. The receptive field of a unit with given weight vector is shifted step by step across input values. The resulting array of subsequent activation events of a unit can provide inputs to higher level units.

The next critical issue is the structure of biomedical devices. The topology of technology should be analyzed in terms of circuit intelligence: nodes, connectivity, type of connections; layers, interfaces between layers and organization of layers. Today, it is hard to perceive a concrete picture of the aforesaid artificial biomedical devices from conceptual stage. The system architecture of the aforesaid biomedical devices is not yet transparent; it is not a simple design. The architecture depends on the mechanism of the biomedical device; and also computing, data, networking, application and security schema.

*Tissue engineering* is expected to be a good solution to the innovation of aforesaid artificial organs (e.g. artificial kidney, liver and pancreas) through cell and gene
therapies. It is an emerging trend of biomedical engineering; the basic building
blocks are cellular, organ and molecular biology, biotechnology, chemical and
mechanical engineering and material science. It may be possible to reconstitute,
maintain, simulate and improve tissue or organ functions in building artificial
organs. It is an interesting research agenda whether it will be possible to replace
physiological functions of diseased tissues and living organs with synthetic
materials, biological compounds and cells. Finally, the expert panel are focusing on
the structure of artificial immune system.

* Artificial immune system

* Intelligent broadcast communication system

o System : Radio, TV, Mobile phones, Social networking system, Knowledge management system;

o The sending agents broadcast data on epidemic assessment and mitigation plan, symptoms and causes of diseases.

o The receiving agents verify the security intelligence of broadcasted data such as false data injection, shilling and Sybil attacks.

* Intelligent analytics

o Real-time online data tracking system

* Real-time data tracking system of available healthcare services (e.g. free beds, paid beds) in hospitals and clinics at a particular zone (e.g. city, rural area, district);

* Real-time data tracking system of stock (or inventory) of medicines in retail outlets of drugs and medicines;

* Real-time data tracking system on epidemic and pandemic outbreak (e.g. [zone, country, state, district], infection, testing, recovery, death, suspected agents, prediction);

* E-governance system : Online grievance or complaint registration and tracking system for municipal corporation and gram panchayet

o Access online portal.

o Input : Enter personal data (name, address, phone number, email address, ward no.), complaint details (infection, death, gaps in municipal functions e.g. cleaning of drains, removal of garbage).

o Output : Get complaint registration number.

o Test, trace and track the status of action by municipal corporation / panchayet against complaint number.

* Telemedicine for treatment of epidemic and pandemic outbreak

Theorem : AIM verifies innate and adaptive system immunity in terms of collective, machine, security, collaborative and business intelligence through multi-dimensional view on intelligent reasoning.

The proposed mechanism (AIM) is defined by a set of elements : system, a group of agents, a finite set of inputs of each agent, a finite set of outcomes as defined by output function, a set of objective functions and constraints, payment function, an optimal set of moves, revelation principle and model checking or system verification
protocol. The proposed mechanism evaluates the innate and adaptive immunity of a
system which is defined by a set of states (e.g. initial, goal, local and global) and state
transition relations.

The mechanism follows a set of AI moves. The basic building block of the
mechanism is an analytics having multidimensional view of intelligent reasoning.
Reasoning has multiple dimensions like common sense, automated theorem proving,
planning, understanding, hypothetical, simulation dynamics and envisioning i.e.
imagination or anticipation of alternatives. The inference engine selects appropriate
reasoning techniques from a list of options such as logical, analytical, case based,
forward and backward chaining, sequential, parallel, uncertainty, probabilistic,
approximation, predictive, imaginative and perception based reasoning depending
on the demand of an application. Another important move is the collection of
evidence through private search which may require a balance between breadth and
depth optimally. It is computationally hard to simulate the intelligence of a detective
with high IQ in a mechanism; can you recall the reasoning style of Ace Ventura or
Sharlock Homes to collect right evidence for solving critical cases!

The critical challenge is how to detect the danger signal from a system? The
mechanism evaluates system immunity (i) combinatorially in terms of collective
intelligence, machine intelligence, security intelligence, collaborative intelligence and
business intelligence. The collective intelligence (a) is defined in terms of scope,
input, output, process, agents and system dynamics. For a complex application, it
verifies coordination and integration among system, strategy, structure, staff, style,
skill and shared vision. What is being done by various components of a system?
Who is doing? Why? How? Where? When? The machine intelligence (b) checks the
system in terms of safety, liveness, concurrency, reachability, deadlock freeness,
scalability and accuracy. For example, it should check preconditions, post
conditions, triggering events, main flow, sub flow, alternate flow, exception flow,
computational intelligence, communication cost, traffic congestion, time and space
complexity, resources, capacity utilization, load, initial and goal states, local and
global states and state transition plans of an information system.

The security intelligence (c) verifies the system in terms of authentication,
authorization, correct identification, non-repudiation, integrity, audit and privacy;
rationality, fairness, correctness, resiliency, adaptation, transparency,
accountability, trust, commitment, reliability and consistency. The collaborative
intelligence (d) evaluates the feasibility and effectiveness of human-computer
interaction to achieve single or multiple set of goals, information sharing principle
and negotiation protocol. The business intelligence (e) looks after business rules
such as payment function, cost sharing, bonus, contractual clauses, quality,
performance, productivity, incentive policy and competitive intelligence.

The basic protocol of AIM is based on bio-inspired artificial intelligence and
immunological theories such as negative selection, danger signal detection, clonal
selection, suppression and hyper mutation. Negative selection is an immune inspired
classification scheme. For a given set of self sensor nodes, it generates a set D of
detectors that do not match with any element of S. Then, these detectors are used to
partition a monitor set M into self and non-self elements. The problem faced by
human immune system is similar to that of information and communication
technology schema. It is difficult to defend a system against a previously unknown
danger. The only reliable knowledge is the normal behavior of the system which is
equivalent to self nodes of the distributed system. Negative selection mimics the
human immune system; it generates a set of detectors that do not match with self
nodes, these detectors are used to monitor the abnormal behavior of the distributed
system caused by the attack of non-self nodes. Danger threatens living organisms;
the danger theory suggests that the human immune system detects danger to trigger
appropriate immune responses. The optimal trade-off between the concentration of
danger and safe signals within human tissues produce proper immune responses.
Danger also threatens distributed computing systems. Danger theory can be
applicable to intrusion detection. It is an interesting option to build a computational
model which can define and detect danger signals. The danger signals should be
detected fast and automatically to minimize the impact of malicious attacks by the
intruders on a distributed system.

The mechanism verifies system immunity through a set of verification algorithms. It
is possible to follow various strategies like model checking, simulation, testing and
deductive reasoning for automated verification. Simulation is done on the model
while testing is performed on the actual product. It checks the correctness of output
for a given input. Deductive reasoning tries to check the correctness of a system
using axioms and proof rules. There is risk of state space explosion problem in case
of a complex system with many components interacting with one another; it may be
hard to evaluate the efficiency of coordination and integration appropriately. Some
applications also demand semi-automated and natural verification protocol. The
mechanism calls threat analytics and assesses risks of single or multiple attacks on
the system under consideration: analyze performance, sensitivity, trends, exception
and alerts; checks what is corrupted or compromised: agents, protocol,
communication, data, application and computing schema? Performs time series
analysis: what occurred? what is occuring? what will occur? assess probability of
occurrence and impact; explores insights : how and why did it occur? do cause-
effect analysis; recommends : what is the next best action? predicts: what is the best
or worst that can happen?

*Theorem*: The computational intelligence is associated with the cost of verification algorithms of system immunity and the complexity of threat analytics.

The verification system requires both automated and semi-automated verification
options. The verification system calls threat analytics and a set of model checking
algorithms for various phases : exploratory phase for locating errors, fault finding
phase through cause effect analysis, diagnostics tool for program model checking
and real-time system verification. Model checking is basically the process of
automated verification of the properties of the system under consideration. Given a
formal model of a system and property specification in some form of computational
logic, the task is to validate whether or not the specification is satisfied in the model.
If not, the model checker returns a counter example for the system's flawed
behavior to support the debugging of the system. Another important aspect is to
check whether or not a knowledge based system is consistent or contains anomalies
through a set of diagnostics tools.
There are two different phases : explanatory phase to locate errors and fault finding phase to look for short error trails. Model checking is an efficient verification technique for communication protocol validation, embedded system, software programmers' , workflow analysis and schedule check. The basic objective of the model checking algorithm is to locate errors in a system efficiently. If an error is found, the model checker produces a counter example how the errors occur for debugging of the system. A counter example may be the execution of the system i.e. a path or tree. A model checker is expected to find out error states efficiently and produce a simple counterexample. There are two primary approaches of model checking: symbolic and explicit state. Symbolic model checking applies to a symbolic representation of the state set for property validation. Explicit state approach searches the global state of a system by a transition function. The efficiency of model checking algorithms is measured in terms of automation and error reporting capabilities. The computational intelligence is also associated with the complexity of threat analytics equipped with the features of data visualization and performance measurement.

The computational intelligence may be associated with four decisions depending on
the type of an application: encoding, similarity measure, selection and mutation.
Antigens and antibodies are encoded in the same way. An antigen is the target or
solution. The antibodies are the remainder of the data. After the fixing of efficient
encoding and suitable similarity measure, the algorithm performs selection and
mutation both based on similarity measure until stopping criteria are met.

The threat analytics analyze system performance, sensitivity, trends, exception and alerts along two dimensions: time and insights. The analysis on time dimension may be as follows: what is corrupted or compromised in the system: agents, communication schema, data schema, application schema, computing schema and protocol? what occurred? what is occuring? what will occur? Assess probability of occurrence and impact. The analysis on insights may be as follows: how and why did the threat occur? What is the output of cause-effect analysis? The analytics also recommends what is the next best action? It predicts what is the best or worst that can happen?

4. SECURITY

Prof. Bob Taylor and Dr, Arvind Gurumurthy are analyzing the security intelligence of emerging healthcare technologies. It is essential to verify security intelligence of the technological innovation associated with cancer prevention collectively through rational threat analytics at five levels : L1, L2, L3, L4 and L5. At level L1, it is important to audit the access control mechanism for the biological system of cancer patients in terms of authentication, authorization, correct identification, confidentiality and data integrity. At level L2, it is required to verify fairness, robustness, correctness, transparency, accountability, trust and commitment. Next, it is required to verify the system performance at level L3 in terms of stability, robustness, reliability, consistency, resiliency, liveness, deadlock freeness, reachability, synchronization and safety. At level L4, it is required to assess
the risks of various types of malicious attacks by adversaries on the human biological system such as Denial of Service (DoS), false data injection attack and sybil attack. At level L5, it is required to assess the risks of various types of corruptions such as agents, system administration and payment function associated with cancer treatment. It is rational to adopt an optimal mix of proactive and preventive i.e. sense-and-respond approaches to fight against cancer; the following section outlines a Deep Analytics based Cancer Prevention Mechanism (DACPM).

## Deep Analytics based Cancer Prevention Mechanism [DACPM]

**Agents:** Defender (e.g. human agent, doctor), Attacker (e.g. malicious agent or adversary);

**Model:** Human biological system – (a) body, (b) mind;

**Objectives:** cancer prevention at optimal cost;

**Constraints:** budget or financial constraint, resources, time, knowledge;

**Input:** Perception of human agent, performance measures of biological system or test data;

**Strategic moves:**

* optimal mix of proactive and reactive approaches;

* deep learning algorithm;

* intelligent reasoning : case based reasoning (CBR), perception, analytical, logical, common sense, biomedical instrumentation;

* rational healthcare payment function and budget plan;

* adaptive secure multi-party computation;

**Revelation principle:** The agents preserve privacy of strategic data;

* **Defender:** The defenders share critical information collaboratively.

* **Attacker:** The adversaries do not reveal the plan of malicious attack, information of targets and weak links in advance.

**Cancer Prevention Approaches:**

<figure><img src="image_35.png" /></figure>

**Proactive approach:**

* Identify targets : computing, data, networking, security and application schema;

* Threat modeling

* Call threat analytics function ($f_a$) and assess miscellaneous risk elements;

* Estimate probability ($p$) of occurrence along two dimensions : Low [L] and High [H];

* Estimate impact of risk i.e. sunk cost (c) along two dimensions : [L,H];

* Map threats into a set of risk profiles or classes : LL, LH, HL and HH;

* Estimate requirements of healthcare in terms of demand plan ($P^p$);

* Explore risk mitigation plan ($P^m$) : accept / transfer / remove / mitigate risks.

* Auto-immunity and vaccination;
* Optimal diet intake (e.g. fruits : amla, vegetables, nutrients) to fight against malnutrition;

* Life-style : Avoid smoking and alcohols, food habit, drug addiction control, wild polygamy, obesity and overweight control through yoga and physical activities, stress control through meditation;

* Self-healing mechanism through wearable computing based health monitoring devices which measure several health parameters in real-time such as pulse rate, temperature, blood pressure, respiration rate, heart bit and oxygen saturation rate;

* Yoga for physical pain and treatment of psychological, social and spiritual trauma (as side effects of radiation and chemotherapy)

* ‘Suryanamaskar’

* ‘Pranayam’ and deep breathing exercises for stress management

* Free hand exercises for muscle relaxation

* standing postures (e.g. ‘chakrasan’, ‘pada hastasan’)

* sitting postures (e.g. ‘ustrasan’ or ‘shashangasan’ or ‘rabit pose’)

<figure><img src="image_36.png" /></figure>

**Reactive approach:**

* adopt sense-and-respond strategy.

* assess risks of single or multiple attacks on the human biological system; analyze performance, sensitivity, trends, exception and alerts.

* what is corrupted or compromised?

* time series analysis : what occurred? what is occuring? what will occur?

* insights : how and why did it occur? do cause-effect analysis.

* recommend : what is the next best action?

* predict: what is the best or worst that can happen?

* verify security intelligence of application, computing, networking, security and data schema of biological system.

* Level1: correctness, fairness, accountability, transparency, rationality, trust, commitment;

* Level2: authentication, authorization, correct identification, privacy, audit;

* Level3: safety, reliability, consistency, liveness, deadlock-freeness, reachability, resiliency;

* Level4: stability, system dynamics, quality of application integration.

* Explore risk mitigation plan ($P^r_d$ and $P^r_m$).
* Do medical testing → Data visualization (Refer Deep Learning Algorithm of section 2.1)

* Treating viral and bacterial infection, chronic inflammation, pain, diabetes, cholesterol and hormonal imbalance;

* Integrated medicine

* Regenerative medicine

* Chemotherapy and radiation

* Laser

<figure><img src="image_37.png" /></figure>

**Fight against bad luck :** Identify critical risk elements.

* Genetic disorder (sex, race, ethnicity, somatic mutation)

* Reproductive disorder (flaws in organ formation and development since birth, personal, hormonal and family history)

* Injuries from accidents, sports and games, war and crime

* Side effects of medical treatment (e.g. hormone therapy)

* Occupational exposure (e.g. at nuclear power plant; alternative option : solar power)

* Environmental pollution (e.g. nuclear and thermal power plant)

* Hostile climate, weather and other locational disadvantages, exposure to sunshine

* Malnutrition due to poverty

* Develop risk mitigation plan in terms of organ transplantation, surgical operation, gene therapy, stem cell therapy and migration of human civilization from risky zone.

**Payment function:**

* Select dominant strategy of healthcare investment from the options of reinforcement on the weakest link, experimental treatment, process re-engineering, transformational and renewal.

* Estimate aspiration point, reservation point, strong, weak, indifference and veto thresholds in healthcare.

* Trade-off proactive vs. reactive security; assign weights to each approach.

* Cost of illness (COI) : health sector costs (direct cost) + decreased or lost productivity by the patient (indirect cost) + the cost of pain and suffering (intangible cost);

* Allocate healthcare budget in the ratio x:y:z where x: fund for proactive approach, y : fund for reactive approach and z: health insurance premium;

* Crowd funding campaign through social media (e.g. pediatric cancer treatment appeal at social networking site or crowd funding portal.

**Output:** Cancer prevention plan

Deep Learning Algorithm

**Objective:** Computer aided cancer detection and diagnosis with improved accuracy;

**Input** : Medical images with optimal number of correct features;
*Data preprocessing*: Filter raw data from noise and incompleteness;

*System Architecture*: Deep Convolution Neural Network (CNN);

*Training strategy*: Ensemble learning

* Create multiple data sets from original training data;

* Build multiple base classifiers (e.g. kNN, SVM, DT, RF, GBDT) for each data set;

* Combine classifiers;

*Algorithm*:

D: Original training data, n: no. of base classifiers, Dᵢ: Test data;

for i = 1 to n do

create training data set Dᵢ from D;

build base classifier Cᵢ on Dᵢ;

end for

for each test data x ∈ Dᵢ

$$C^*(x) = C_1(x) \oplus \dots \oplus C_n(x); \text{ /* } \oplus \text{ : combination operator */}$$

end for

*Testing strategy*: Predictive accuracy analysis;

*Output*: Pattern recognition for cancer location identification, cancer tissue classification, cancer image segmentation, cancer image retrieval, big image data analysis

DACPM is basically a security game i.e. fight against cancer. It is defined by various types of elements: a group of agents, model, actions, a finite set of inputs of each agent, a finite set of outcomes as defined by output function, a set of objective functions and constraints, payments, a strategy profile, a dominant strategy which maximizes the utility of an agent for all possible strategies of other agents involved in the mechanism, security intelligence and revelation principle. There are two agents in the security game: a defender (D) and the attacker (A). Each agent adopts and executes a or a set of strategies. A pure strategy is a deterministic policy for a single move game. For many games, an agent can do better with a mixed strategy. The best strategy may depend on the knowledge of the defender about prospective attacks and the sunk costs incurred when upgrading information security schema reactively. The payment function of the mechanism estimates an optimal investment plan for the protection of human biological system. The mechanism verifies the security intelligence of human biological system; it is a multi-dimensional parameter which is defined in terms of rationality, fairness, correctness, resiliency, adaptation, transparency, accountability, trust, reliability, consistency, commitment; safety, liveness, synchronization, reachability, deadlock freeness; authentication, authorization, correct identification, non-repudiation, integrity, audit and privacy.

The DACPM mechanism evaluates security intelligence of the human biological system based on proactive and reactive approaches. The system is expected to be a resilient system. The resiliency measures the ability to and the speed at which the information system can return to normal performance level following a disruption. Adaptability is about responding to change effectively and decisively through reactive approach: the ability to identify the change in search space for the adversaries, understanding the probable impacts of the hit by the adversaries, rapid
quantification what is under its control to compensate, identification what modifications to the environment are necessary and adoption of risk mitigation measures in time without any hesitation. The vulnerability of the system to a disruptive event such as cancer should be viewed as a combination of likelihood of a disruption and its potential severity. The defender must do two critical tasks: assess risks and mitigate the assessed risks. To assess risks, the defender should explore what can go wrong in the operation of the system? what is the probability of the disruption? how severe it will be? what are the consequences if the disruption occurs? A vulnerability map can be modeled through a set of expected risk metrics, probability of disruptive event and the magnitude of consequences.

The defender tries to define the payment function associated with healthcare in terms of aspiration point, reservation point and adjustment of various preferential thresholds (e.g. indifference, strong preference, weak preference, veto) and preferred solutions. Cost of illness may be estimated based on direct cost (e.g. health sector costs), indirect cost (e.g. loss of productivity of the patient) and intangible cost (e.g. cost of pain and suffering). Direct costs include hospitalization, medication, emergency transport, and medical care. Decreased or lost productivity may be the result of illness, premature death, side effects of illness or treatment or time spending receiving treatment. With premature death, the indirect cost is the loss in wage and benefits.

The computational cost of deep learning mechanism depends on the complexity of threat analytics function, deep learning algorithm and payment function. The cost of computation is a function of the complexity of threat analytics. The threat analytics analyze system performance, sensitivity, trends, exception and alerts along two dimensions: time and insights. Another major computational burden of the deep learning mechanism is the complexity of verification or model checking algorithms. The cost of computation also depends on the complexity of payment function. The appendix shows the application of DACPM for nine types of cancer as stated in scope (appendix).

It is essential to design biomedical devices in terms of security at various levels – L₁, L₂, L₃, L₄ and L₅. Level L₁ verifies system performance in terms of correctness of measurement of health parameters by sensors, safety, reliability, consistency, stability and robustness. The device should be safe. Safety is the most important consideration in the design of any biomedical device. Let us consider the safety aspects of artificial kidney in terms of biological, chemical, mechanical and human factors. The device should have high biocompatibility and blood compatibility; the device should not cause hemolysis i.e. destruction of red blood cells. Water quality is a major safety concern for dialysate fluid. The dialysis membrane should have high shear and ultimate strength. The device should be easy to operate and should be fool-proof. The design of artificial limbs is expected to satisfy several critical requirements such as subconscious control, user friendliness for simple learning and use, independence in multifunctional control, parallel control of multiple functions, direct access, speed of response, no sacrifice of human functional ability and natural appearance.
## Security analytics for biomedical technology

* Level 1: System performance – correctness, reliability, consistency, stability, robustness, resiliency, liveness

* Level 2: Access control – authentication, authorization, correct identification, audit of quality control issues

* Level 3: Design schema – safety, biological safety, mechanical safety, chemical safety, human factor safety, rationality, transparency, accountability, fairness, correctness

* Level 4: Corruption – computing, data, application, networking schema

* Level 5: Malicious attack – false data injection attack, data traffic congestion, denial of service

The other critical design issues are also associated with resiliency, deadlock-freeness, synchronization and interactive intelligent communication protocol. The safety of bio-medical devices depends on access control at level L₂ in terms of authentication, authorization, correct identification of system components and audit of quality control issues. The security schema is also designed and verified at level L₃ in terms of rationality, fairness, transparency, accountability, trust and commitment.

The safety of the bio-medical devices may be threatened at level L₄ through corruption of computing, data, application and networking schema. The design of the bio-medical devices is also expected to assess and mitigate the risks of various types of attacks at level L₅ such as false data injection, shilling and data traffic congestion. A biomedical device should be safe from the perspectives of biological, chemical, mechanical, human factor safety. Safety is one of the most important design criteria. A biomedical device should have high biocompatibility and blood compatibility. For example, an artificial kidney should not result hemolysis i.e. destruction of RBC; should not adsorb or filter blood cells; should not introduce any foreign toxic materials into blood; also remove toxic material. The quality of water is a major safety concern for dialysate fluid. The dialysis membrane should have high shear and ultimate strength and dimensional stability. A biomedical device should be easy to operate and should be fool-proof. Alarms should be incorporated into a biomedical device the dialysis machine to signal any malfunctioning of the system components.

What is bio-terrorism? This is a question of life and death in human society globally. Traditionally, it is the intentional use of biological or chemical agents to cause disease or destroy food and water supplies or capture and kill human agents for political or economic reasons to achieve malicious business intelligence [28]. Today, it is difficult to classify the illnesses caused by biological, chemical and radiological weapons from naturally occurring ailments. Our society is going through an excellent progress of life-science supply chain management and healthcare service and medical practice. But, there is threat of bio-terrorism on the soft targets such as life-science supply chain and healthcare service chain. Bio-terrorism is basically a management game in today's business world. Bo-terrorism is generally related to the use of biological, chemical and radiological weapons in the battle fields.
surprisingly, the definition of bio-terrorism has been changed from the use of bullets and explosives towards slow poisoning of the innocent public through innovation of new viruses, anti-viruses, drugs and toxic tasty junk food, soft drinks and beverages, flawed digital bio-medical instrumentation and unethical medical practice. It is a silent trap of death. The conflicts between security intelligence and business intelligence are inevitable. Today's healthcare system must satisfy the basic requirements of security and safety for the benefits of the common people of the world. The analytics must explore the risk of all possible threats on the soft targets. Our society needs rational decision support system, microsoft healthcare policy and innovative medical practice to resist bio-terrorism. Is it a mission impossible? The following section outlines today's bio-terrorism mechanism transparently.

**Agents:** Service provider (P), Service consumer (C), Partners of life-science supply chain, Adversaries;

**Input:** Data on agents, healthcare products and services;

**Targets:** Life-science supply chain, Healthcare service chain;

*   **Life-Science Supply Chain**

*   **SCOR**

*   Planning: demand, inventory, production and capacity, packaging and labeling, MRP;

*   Collaboration and sourcing;

*   Execution -sales and distribution, order management, warehouse management, transportation management, reverse logistics;

*   **DCOR:** innovation, product life-cycle management, pricing;

*   **CCOR:** customer relationship management;

*   **Healthcare Service Chain:** registration, consulting, testing, surgical operations, billing, payment processing and follow-up;

**Threats:** call threat analytics and assess risks;

*   Management Information System (MIS) corruption

*   False data injection attack

*   Sybil attack

*   Private data leakage

*   Flawed web security

*   Flawed Knowledge Management System for creation, storage, transfer and application

*   Broadcast corruption through shilling attack

*   Technical snags, unreliability and inconsistency in digital bio-medical instrumentation

*   Biotechnology based innovation of new germs, viruses and anti-viruses

*   Life-science supply chain corruption

*   Good Manufacturing Practice (GMP)

*   Total Quality Management (TQM)

*   Social Choice Problems (e.g. crimes, kidnapping)

*   Poor quality of service in healthcare service chain
* Transaction processing system

* Decision support system and GDSS

* Business intelligence system

* Malicious data mining

* Insecure data storage

* False data visualization

* Poor performance measurement

* Malicious business intelligence

* Greedy heuristics in payment function for revenue and profit optimization

* Economic pressure and incentive policy

* Fraudulent health insurance model

* Investment for technology management

* Irrational and dull HR policy in talent management

* Chaotics in formulation of public policy, mechanisms, regulatory compliance and corporate governance

**Security intelligence verification:** call intelligent threat analytics, verify security intelligence at multiple levels.

* Level 1: audit computational intelligence in terms of correctness of computation and rationality of filter configuration,

* Level 2: verify system performance of the adaptive filter in terms of reliability, consistency, resiliency and liveness;

* Level 3: malicious attacks – verify the risks of Denial of Service (DoS), false data injection, intrusion and Sybil attack on adaptive filter;

* Level 4: multi-party corruption – assess the risks of corruption of system administrator and filtering mechanism of adaptive filter;

* Level 5: verify the efficiency of access control of adaptive filter in terms of authentication, authorization, correct identification, privacy, audit, nonrepudiation, confidentiality and data integrity;

**Strategic moves for risk mitigation:** Scope, System, Structure, Security, Strategy, Staff-resources, Skill-Style- Support (governance and regulatory compliance, Shared vision, Service and Social networking);

**Output:** Plan to counter bio-terrorism

## THREAT ANALYTICS

**Theorem:** *The security intelligence is explored through threat analytics on bio-terrorism.*

It is essential to verify the security intelligence of the target system (i.e. life-science supply chain and healthcare service chain) in terms of correctness, fairness, rationality, trust, transparency, accountability, reliability, consistency, confidentiality, data integrity, non-repudiation, authentication, authorization, correct identification, privacy, safety and audit. It defines the security intelligence of the system comprehensively with a novel concept of collective intelligence.
In bio-terrorism mechanism, the soft target points of adversaries may be life-science supply chain and healthcare service chain. An intelligent analytics explore miscellaneous types of threats on the targets such as information system corruption through false data injection attack, sybil attack, private data leakage and knowledge management; broadcast corruption through shilling attack; technical snags, unreliability and inconsistency in the performance of digital bio-medical instrumentation; poor quality of service in registration, consulting, testing, surgical operation, billing and payment processing of healthcare service chain; flaws of transaction processing, decision support, group decision support and business intelligence system : malicious data mining in testing, insecure data storage, data visualization techniques and performance measurement; life-science supply chain corruption in planning, collaboration, execution, sales and distribution, product life-cycle management; malicious business intelligence in the modes of greedy heuristics in payment function for revenue and profit optimization, economic pressure and irrational incentive policy, fraudulent health insurance model and financial intelligence, incorrect decision on investment for technology management; biotech innovation of new jerms or viruses and anti-viruses and finally irrational and dull human resource management policy in talent management, reward and recognition. This list is not exhaustive. There are other several critical factors such as non-cooperative corporate culture, power play and politics, ethics, strategic blunder and chaotics in formulation of public policy, mechanisms and corporate governance. It is an open research agenda.

Bioterrorism mechanism explores different scenarios of corruption in terms of service provider, consultant, service consumer, life-science supply chain partners, system administrator and adversaries. The system results correct and fair output if all the agents, communication channel and data are free of corruption. Corruption may occur in various ways. The adversary is capable of corrupting an agent, input or output data. The corruption strategy indicates when and how parties are corrupted. In case of static corruption model, the adversary is given a fixed set of parties whom it controls. Honest parties remain honest throughout and corrupted parties remain corrupted. In case of adaptive corruption model, adaptive adversaries are given the capability of corrupting parties during the computation. The choice of who to corrupt, and when, can be arbitrarily decided by the adversary and may depend on its view of the execution. The basic objective of the threat analytics is to assess risks of different types of malicious attacks and explore risk mitigation plans accordingly.

The mechanism uses 10-S model as a set of strategic moves of mitigating the risk of aforesaid threats: System, Security, Strategy, Structure, Staff, Skill, Style, Shared vision, Service and Social networking. It is a hard problem. It requires proper coordination and integration among ten elements associated with life-science supply chain and healthcare service chain. The basic objective is to ensure that different organizational elements of the healthcare service provider are appropriately synchronized and work in harmony to improve the quality of service. The hard elements are strategy, organizational structure and system. The soft elements are leadership style, resources, skill, security and shared values. The hard elements can be identified and defined in a relative simple way and directly controlled by
corporate management. The soft elements are difficult to define, less tangible and influenced by corporate culture. The strategic fit among these factors is the basic building block of organizational effectiveness. A system acts rationally against bio-terrorism when these ten elements are aligned correctly and mutually reinforcing.

**Theorem**: Technology management is a critical threat factor responsible for bioterrorism through corruption of management information system, corrupted communication schema, malicious bio-medical instrumentation and biotech innovation.

(Case-1.1: Biomedical instrumentation): This is the case of technical snags, unreliability and inconsistency in digital bio-medical instrumentation. Digital transformation requires the intelligence of biomedical engineering, bio-sensors, bio-inspired artificial intelligence and human computer interaction for improved QoS in patient care. A smart healthcare information system should be correctly integrated with bio-medical system appropriately through sensors, robotics, human computer interaction, mobile communication system and internet. An effective digital transformation enables the service provider to offer different innovative patient care services through medical imaging systems, digital radiography, computed tomography, nuclear medicine, computer-integrated interventional medicine, ultrasonic imaging, magnetic resonance imaging, diffuse optical imaging, image compression, medical image retrieval, parametric imaging, brain magnetic resonance imaging, molecular imaging, data processing and analysis by electronic medical record (EMR), image registration, biological computing, picture archiving, medical imaging informatics, digital library, integrated multimedia patient record systems, computer-aided diagnosis and clinical decision support systems. The patients or healthcare service consumers can receive innovative healthcare services through digital transformation such as mobile electrocardiogram (ECG) recording, portable defibrillators, digital X-ray, digital stethoscopes, digital pulse monitoring watches, blood pressure monitors, blood sugar monitors, pacemakers, pregnancy and urine test kits.

The most serious threat of digital biomedical instrumentation is inconsistent and unreliable malfunctioning of the devices and instruments due to intentional and planned attacks by the malicious agents. They may configure the devices incorrectly. There may be problems of regular and preventive maintenance of these devices. A maliciously configured or malfunctioning digital stethoscope or pulse watch may declare a live patient as dead. Attestation verification of biomedical devices is a critical requirement of a smart healthcare service chain as a part of regular and preventive maintenance: check if a device is tampered by an adversary; check the configuration and correct setting of each device; detect whether malicious software is loaded into sensor nodes; verify the integrity of the code; perform secure code updates and ensure untampered execution of code. Each device should be attested with a valid digital test certificate. The verification algorithm must verify the identity and tampering status of each device. The basic objective of device attestation is that a malicious agent should not be able to configure or change correct setting of a device. A challenge response protocol should be called between a
trusted external verifier and a biomedical instrument on periodic basis to check the reliability, consistency and correctness of the system. Bio-medical instruments must use efficient pattern recognition algorithms for pattern recognition such as support vector machine for pattern classification, clustering and association rule mining.

(Case-1.2 : Biotechnology): This is the case of biotechnology based innovation of new germs or viruses and anti-viruses. New types of jerms are explored and injected into human body through different common channels such as natural disaster (e.g. rainfall, flood), environmental pollution (e.g. air, water, soil), water supply, saline, drugs and pathological tests. It optimizes the revenue of drug companies through increased sale of anti-viruses. On the other side, it reduces the life-cycle of the patients or service consumers. It is basically a malicious management game. Cross-border medical terrorism is becoming a common occurrence in human society today; it may be across countries, inter-state, inter-district, inter-city and inter-villages. The migrants are often subjected to new drugs, medical treatment and surgical operation procedures for risky experiments in medical science. Many people are losing their life prematurely due to such terrorism. The experiment should be done on other animals through intelligent simulation game. The critical causal factor of such type of bio-terrorism is erosion of social values and quality of education at branded technical, medical and management institutes, R&D laboratories and mult-tiered life-science supply chain. The scientists innovate new medicines and biomedical instruments for the benefits of the human society; the business world often uses the innovation maliciously for greater sales, revenue and profit. It requires close watch, monitoring and audit of the activities of these institutes privately and continuously for the good of common people. Absolute autonomy in system administration and freedom of expression may result horrific disasters and chaotics in human society today.

(Case-1.3: False data injection attack): False data injection attack broadcasts incomplete, corrupted, noisy, got up and incorrect data through intrusion of malicious agents or corrupted sending agent and affects the reliability of corporate communication system. False data injection attack is very common through broadcast such as reproduction mechanisms, sports, body building and sex medicines, joint and knee pain killer medicines and beauty care products (e.g. skincare, hair care) without mentioning the side effects of different chemical compounds and herbal ingredients used in the medicines on human health and mind. A patient can be easily confused and cheated through popular broadcast and digital advertising. For example, detailed ingredients and chemical compositions are not mentioned clearly on the packet of drug Z for hair care though it is very costly product. The public are consuming energy capsules unknowingly and getting attacked with ailments of digestion system, liver and kidney.

The service consumers must verify the fairness, trust and correctness of data communicated by the service provider to detect false data injection attack and mitigate the risk through social choice. The verification mechanisms require the intervention of trusted third parties or detectives who should arrest the malicious agents. The recipients must adopt tit-for-tat strategy: honest public campaign
against fake broadcast, threats and punishments of the adversaries. The service
consumers must verify the quality of broadcast and provide true, honest and
intelligent feedback to the broadcasting forum. If the forum is inactive, toothless,
clawless and casual, the deceived agents should report to the highest authorities and
seek for legal help to corporate governance. The recipients may adopt retaliative
moves such as rejection of fraud channels or switching from one service provider to
the other for better quality of service.

(Case-1.4: Shilling attack): This is the case of broadcast corruption through shilling attack. Adaptively secure broadcast is essential to resist shilling attack. Malicious broadcast is a real threat to the digital advertising world. If the recipients sense flaws in digital advertising, the system administrator must verify the correctness, fairness and transparency of the system through analytics on ad slot allocation, content of adwords, exposure time and frequency, customization, delivery, click rate, and impression. Today's broadcast is closely associated with advertising as a recommender system. But, there is risk of shilling attack in the form of push and nuke attacks where the rating of target items (e.g. drugs, medicines and cosmetics) are increased and lowered successively. The advertising world may be digitally divided with a flavor of revenge and retaliation due to zero or low investment on advertising by the corporate world. A corrupted broadcasting system may be involved in brand dilution of a good company through baseless, mischievous and false propaganda. Alternatively, the broadcasting system can push a set of targeted items of poor quality and brand to the public through fraudulent adwords, euphemism and attractive presentation of the popular brand ambassadors. But after the disclosure of the information on such types of malicious attacks, the recipients may lose their trust in the adwords of the digital world in future.

This is the most dangerous threat on a broadcasting system where the sender and
the recipients may be honest but the sources of broadcasted data are corrupted. The
recipients must threaten and refuse false adwords and complain to the broadcasting
forum, quality control and detective agencies and government authorities in time
against fraudulent business intelligence. The profiles of shilling attackers must be
deleted with the help of collaborative filtering and efficient ranking system. The
problem should be solved through regulatory compliance (e.g. RTI, consumer
protection acts), cryptology and network security jointly.

(Case-1.5.1 : Sybil attack on cancer treatment): This is the case of cancer treatment
today. The patients are treated incorrectly and diagnosed as cancer casually though
there is another simple medical problem. Natural intuition and perception are not
applied for simple medical problems. The patients are incorrectly recommended for
costly treatment. They are often recommended for costly treatment procedure
repeatedly (e.g. CT scan, X-ray), drugs and surgical operations. The poor and
helpless patients are forced to validate and verify the test reports and medical
diagnosis at various healthcare institutes. This is an instance of modern biological,
chemical and radiological terrorism. Fairness and correctness of computation and
testing is a critical concern in healthcare practice. Knowledge management is
another critical success factor; case based reasoning may be a good solution for correct clinical decision making.

(Case-1.5.2 : Sybil attack on herbal and unani drugs): Herbal and Unani drugs are often sold at very high price in the market in the form of sophisticated labels and packaging of scientific drugs. The poor patients are forced to spend high amount of money on account of fake drugs. The consumer protection ministry should detect this fake drug racket to save the people. If a herbal or ayurvedic or unani drug is really effective for a healthcare problem; it should be recognized through proper channel, marketing, branding, pricing, promotional and distribution strategy. It is really complex to trace the corrupted players in the broadcast. A broadcasting communication network is defined by a set of entities, a broadcast communication cloud and a set of pipes connecting the entities to the communication cloud. The entities can be partitioned into two subsets: correct and faulty. Each correct entity presents one legitimate identity to other entities of the distributed system. Each faulty entity presents one legitimate identity and one or more counterfeit identities to the other entities. Each identity is an informational abstract representation of an entity that persists across multiple communication events. The entities communicate through messages. A malicious agent may control multiple pseudonymous identities and can manipulate, disrupt or corrupt a distributed computing application that relies on redundancy by injecting false data or suppressing critical data it is sybil attack.

There are various types of tracing mechanisms against sybil attack: trusted explicit and implicit certification, robust authentication, resource testing and incentive based game. In case of trusted certification, a centralized authority assigns a unique identity to each entity. The centralized authority verifies computing, storage and bandwidth capability of the entities associated with the broadcasting system on periodic basis. The recipients validate the received data from the sender and checks logically whether there is any inconsistency or chance of injection of false data in the decrypted message. Another approach of tracing is to adopt incentive based game wherein the objective of the detective is to compute the optimum possible reward that reveals the identity of maximum number of corrupted agents. A local identity (l) accepts the identity (i) of an entity (e) if e presents i successfully to l. An entity may validate the identity of another identity through a trusted agency or other entities or by itself directly. In the absence of a trusted authority, an entity may directly validate the identities of other entities or it may accept identities vouched by other accepted entities. The system must ensure that distinct identities refer to distinct entities. An entity can validate the identity of other entities directly through the verification of communication, storage and computation capabilities. In case of indirect identity validation, an entity may validate a set of identities which have been verified by a sufficient count of other identities that it has already accepted.

(Case-1.6 : E-healthcare): E-health is a significant development of the use of emerging information and communication technologies i.e. internet in the healthcare environment. E-health while promising also presents new business challenges in terms of acceptable standards, choice of technologies, overcoming
traditional jurisdictional boundaries, upfront investment, privacy and confidentiality. New and evolving information and communication technologies are being adopted by healthcare sector worldwide. It is becoming a commonplace for healthcare systems to deploy an e-healthcare architecture that consists of extranet, intranet and the internet to create a seamless access to data across a set of distributed healthcare systems.

E-healthcare is able to promote bio-terrorism intelligently through many ways. The content of medical and healthcare websites must be carefully designed and audited to resist false data injection attack on the details of drugs, medicine, treatment procedure, doses and side effects. It may not be possible for the common public to treat their ailments just through web search. Clinical decision making is a complex and multi-dimensional problem which needs specialized skill, training and experience of the healthcare consultants. An website should provide detailed information on the availability of healthcare institutes, consultants, distribution and retail outlets and offered healthcare services to the common people correctly. But, it is risky to give detailed data on medical treatment procedure which may confuse the public to take correct clinical decisions.

The model checking algorithms must verify a set of critical parameters such as the risk of snooping and phishing, validation of service oriented computing schema in terms of logic, main flow, sub flows and exception flows of the application, cross site scripting, injection flaws, malicious file injection by testing application programming interfaces and code, insecure direct object reference, cross site request forgery, information leakage and improper error handling, broken authentication and session hijack, insecure cryptographic storage and failure to restrict URL access.

Secure service oriented computing should protect the users from various types of malicious attacks. Phishing is the misrepresentation of a web link where the malicious agents use social engineering to appear as a trusted identity. They leverage the trust of the users to gain vulnerable information via web link, e-mail or instant messages. The phishers try to capture usernames, passwords and other private information of the users by attacking social web sites, auction sites and online payment processors and cause damages ranging from denial of service to financial loss. There are different types of phishing techniques such as link manipulation, filter evasion, website forgery and cross site scripting. The common resolution techniques are user training and the security measures to be embedded in SOC. The users should be able to recognize phishing attempts and should follow privacy and security policies and license agreements. Anti-phishing measures should be embedded in browsers and website login procedures. The website should be authenticated such a way that it indicates that the connection is in authenticated mode and confirms the authenticity and correctness of the particular site to the user.

In case of cross site scripting [XSS], the attacker executes malicious script in the browser of a user and may hijack the session of the user, deface web site and introduce worms. XSS flaws occur whenever an application takes the data from the user and sends it to a web browser without validating or encoding the content. Preventing XSS requires a secure architectural approach such as input validation,
strong output encoding and monitoring of canonicalization errors on a regular
basis. The system should validate the configured business rules and reject invalid
inputs. The data supplied by the users should be appropriately entity encoded. The
inputs must be decoded and canonicalized to the internal representation of the
application before proper validation. *Cross site request forgery* attack forces the
browser of an active user to send a pre-authenticated request to an web application
and forces the victim's browser to perform a malicious action for the benefit of the
attacker. The system should not rely on the credentials or tokens being
automatically submitted by the browsers. Reauthentication of sensitive data is
required to verify a genuine request.

SQL, HTML and XML *injection flaws* occur when an interpreter receives the data
from a user as a part of a command or query; the malicious data force the
interpreter to execute unintended commands and the attackers can create, read, edit
or delete any data. SOC should support a standard input validation mechanism,
strongly typed parameterized query application programming interfaces and object
relational mapping libraries. The application should avoid dynamic query interfaces
and simple escaping functions and should monitor canonicalization errors.
Malicious file execution allows the attackers to perform remote code execution,
remote root kit installation and total system compromise. SOC should use an
indirect object reference map and strongly validate user's inputs. The firewall rules
should prevent the web servers making new connections to external web sites and
internal systems. Insecure direct object reference occurs when a developer exposes a
reference to an internal object like file, directory, database record or key. The
attackers can manipulate those references to access other objects without
authorization. SOC should not expose private object references to the users, validate
any private object reference and verify authorization to all reference objects.

A service oriented application should not disclose their configuration and private
functions through error message, debugging and path information or other means.
This requires an efficient exception handling approach. The other critical issues are
broken authentication, session management and insecure cryptographic storage.
Proper authentication and session management is critical to secure service oriented
computing. Account credentials and session tokens should be properly protected so
that the attackers can not compromise passwords or keys or to assume the identities
of the users. Secure SOC should properly authenticate the users and protect their
identities and associated credentials. Attackers may use weakly protected data to
conduct identity theft. The application should encrypt sensitive information in a
data warehouse. The link of a web application should not be disclosed to the
unauthorized users. Proper access control should be enforced in the presentation
layer and the business logic for all URLs in the application.

*Theorem :* *Life-science supply chain is a soft target of bio-terrorism. The drugs and medicines sold through popular distribution channels may be tainted, compromised and mislabeled. It needs strong support of drug quality and security act.*

The life-science supply chain has developed and produced breakthrough drugs and
medicines that enhance the average life span in the world. Unfortunately, when bad
things happen in life-science supply chain, the public get hurt. Product recalls are often seen in life-science supply chain. During November 27–December 31, 2013, there were eight recalls by FDA in USA. Today’s life science supply chain requires an effective ‘Drug Quality and Security Act and Standards’ which is able to clarify with transparency the authority, roles and responsibilities of food and drugs administration and consumer protection ministry, regulate pricing of drugs, develop a national track-and-trace system to audit the functions of the life-science supply chain and minimize the risks of contamination, adulteration, diversion or counterfeiting.

A complex life-science supply chain is a network of firms that satisfies the demand of the customers for healthcare products and medical services. The basic objective is to improve the quality of service in patient care by integrating different business units through systematic coordination of material, information and financial flows. In the context of bio-terrorism, the target points of a complex multi-tier life-science supply chain are SCOR, DCOR and CCOR. SCOR includes supply chain planning in terms of demand forecasting, inventory control, production and capacity planning, packaging and labeling, materials requirement planning (MRP); supply chain collaboration and sourcing; supply chain execution through sales and distribution planning, order management, warehouse management, transportation management and reverse logistics. DCOR includes innovation, product life-cycle management (PLM) and pricing and CCOR includes customer relationship management. A malicious agent is able to launch attacks on a life-science supply chain through these channels. The following cases support the reasoning of the theorem.

(SCOR Case-3.1 : Corrupted Packaging and Labeling) : Misleading and confusing content, statistical jugglery and data mining on packaging and label of medicines and drugs distributed through consultants, retailers, wholesalers, distributors and other common channels : It is observed on various products such as (a) hair care products, (b) nutritional food supplement i.e. multi-vitamin, multi-minerals tablets. This is a fundamental problem of supply chain management of life-science products. Herbal and Unani drugs are sold at high price in retail outlets in the camouflage of allopathic medicine.

(a) Drug X: It is prescribed to the aged patients as a multi-vitamin, multi-minerals capsule. The detailed data on ingredients are not clearly mentioned on dark red colored label and packet of drug X. Who is verifying and validating the authenticity of ingredients and chemical compositions of drug X? The consumers suffer from constipation and gastric problem after regular use of drug X. They have become suspicious about the side-effects and authenticity of the drug. There may be cascading side-effects due to the use of various drugs on human body and mind. For example; drug X causes side-effect as gastric and constipation problem; Drug Z is used to resist this side-effect and causes another side effect of ulcer.

(b) Drug Y: On its packet, the ingredients are mentioned as Zinc Sulphate Monohydrate, Copper Sulphate Pentahydrate, Borage oil, Gamma Linolenic acid, Selenium and others. Are these elements good for human health? Do they have radioactive effect or any other side effects on human cell? It is not clearly mentioned
whether this medicine can be used for hair care though the picture of hair of a
young man is shown on the packet. It is also confusingly stated that the product is
not intended to diagnose, treat, cure or prevent of any disease and the product is
proprietary food dietary supplement not for medicinal use. It is also mentioned not
to exceed the stated recommended daily dose. But, the dermatologists and hair care
specialist prescribe the product for hair care treatment. Why the label is not clearly
informing that it is a hair care product or a product suitable for chemotherapy
treatment; who should not use the products; what are the adverse side effects of the
use of the medicine? The information should be transparent and accountable and
also trust worthy. There is no short pamphlet or product brochure in the packet of
drug Y. Both drugs X and Y have color such as orange and light blue. When the
tablets are put in normal water, colors are mixing with water. Are these color
pigments enhancing the risk of cancer of the consumers?

(SCOR Case-3.2 : Corrupted Sales and Distribution System) : This is the case of corrupted sales and distribution system of toxic and tasty junk food and beverages in the open market. The public including the children regularly suffer from the sale of junk food in the open market such as banana chips, spicy biriyani, masala paste, mughlai roll and cutlet, herbal drugs, country liquor, noodles, pasta, colored soft drinks, colored sweets, chocolates, pickle, tobacco products (e.g. gutkha, biri) and fast food and snack items and often affected with cancer, stomach kidney and liver upset and digestions problems due to the presence of artificial preservatives, color and flavor. In an industrial town, it is often seen the association of a meat slaughter shop (e.g. chicken, mutton, fish), non-vegetarian food outlet (e.g. biriyani) and liquor shop side by side. The poor and uneducated laborers of factories consume such junk food and beverage and suffer from hepatitis, jaundice, liver and pancreatic cancer and die prematurely. There is no action from consumer protection department to ban the sales and distribution of these malicious elements in open market. Fraudulent advertisements of cosmetics and creams are often broadcasted on beauty care products; are they really effective? There should be more focus on the nutrition of the children, boys, girls and the youth of the world for their proper health and development of body and mind through yoga, meditation, sports and games. They should be able to lead a good life-style and stress free life. Price inflation of essential food and beverage items often cause malnutrition of the poor people. Nutrition, education, adoption of best healthcare practices, sustainable development programs, common sense on maternity and childcare are very important for an innovative healthcare programme management. What is the responsibility of SD module of ERP system in life-science supply chain management? The solution is strict regulatory compliance imposed by sensible and responsible corporate governance.

(SCOR Case-3.3 : Flawed Inventory Planning): Inventory management is a critical
challenge in life science supply chain due to uncertainty of demand and short time
interval between manufacturing date and expiry date. The healthcare consultants
are often pressurized by the sales people of life-science firms to push new or slow
moving drugs, medicines and bio-medical devices of old models for the use of service
consumers. Today, the healthcare consultants often prescribe drugs and medicines based on the availability of stock in the retail outlet. Analytics should be intelligently used for efficient inventory management to monitor dead stock, slow moving stock, ABC analysis and XYZ analysis. It is essential to minimize the wastage of blood and donated organs through improved storage system. Intelligent heuristics should be used to reduce overstock or stock out situation. Example: EOQ = Forecasted demand + Safety stock – Pending order – Current stock. The overstock should be distributed to the poor and needy people in time through efficient distribution planning. The basic objective is to attain approximately zero wastage.

(DCOR Case-3.4 : Inhuman PLM): Product life-cycle management (PLM) is a critical part of life-science supply chain. The concept of threshold heuristics is maliciously adopted to limit the life-cyle of senior citizens as a part of artificially intelligent public health and inhuman economic policy in many developing countries. For example, if an old man is above 75 years old; he will be ill treated in hospitals hook-or-by-cook to ensure death through modern killing machines. Basically, it violates the fundamental human rights of living. Still today, effective medicines, drugs, biomedical devices, surgical operations and treatment procedure are not discovered for different life-threatening ailments such as diabetes, cancer, cancer of mind, migraine and headache rheumatic arthritis, joint and knee pain and orthopedic problems of aged people. In case of diabetes, there is no permanent solution for recovery. A diabetic patient is forced to take medicines continuously for blood sugar control within safe limit. If he / she stops medication, the blood sugar will be out of control. This is the failure of modern medical science. The life-science supply chain needs innovation in product life-cycle management on drugs, bio-medical devices and medical practice.

E-health is a promising IT platform of healthcare services. An efficient information system integrates various enterprise applications such as life-science supply chain and healthcare service chain while maintaining individual autonomy and self-governance. The system should support confidentiality, message integrity, non-repudiation, auditing and availability of service in time. The system should support sharing of data in a collaborative business environment wherein a group of trading agents can exchange strategic business information maintaining the privacy of critical data. Increased organizational agility is required for the cooperation of adaptive enterprises. Information technology can improve the quality of service and reduce cost in healthcare services. The demand for critical patient care is growing. But, many small rural healthcare centers are facing problems to develop and maintain a costly IT infrastructure. This forces those healthcare centers to search for innovative IT platform. A life-science supply chain can be protected from threats of disruption through intelligent coordination mechanisms, intelligent contracts, multi-party negotiation, economic modeling, buyer-seller behavior, security and privacy of the trading agents, payment determination, dynamic pricing strategy and information flow. E-health provides different types of benefits in terms of greater geographical reach, global sourcing, reduced transaction costs, improved customer service, accuracy, ease of processing, increased productivity and quick access to information.
**Theorem 4: Healthcare service chain is another soft target of bio-terrorism.**

The critical processes associated with a healthcare service chain are registration, consulting, testing, surgical operations, billing, payment processing and follow-up. Generally, different types of information systems are commonly used to support these processes: transaction processing system (TPS), decision support system (DSS), group decision support system (GDSS), knowledge management system (KMS) and business intelligence (BI) system. The primary objective of these information systems is to ensure fairness and correctness in computation of registration card, appointment slip for consulting, prescription by consultant, surgery schedule, quality control certificate, medical test report, discharge certificate, bills and payment receipt, feedback form and patient's guide. The other important issue is to preserve the privacy of patient's personal and medical data. There may be the risks of failure of secure multi-party computation due to following reasons:

(Case-4.1) Incorrect data provided by the service consumers or patients to the registration associate during registration intentionally or due to lack of knowledge or incorrect perception of the patients or their attendants; the patients or their attendants may be irrational in information sharing properly with the service providers.

(Case-4.2) No verification of patient's identity correctly during registration; the cases of emergency situation or accidents may skip verification due to unavailability of data about the patients.

(Case-4.3) Wrong entry of data into various information systems by the healthcare associates due to time and resource constraints or misunderstanding or lack of validation of input data.

(Case-4.4) Computational errors due to wrong configuration of enterprise applications and / or errors in the heuristics, algorithms and quantitative models and / or no updating of data (e.g. service charge, tariff of testing, price of drugs and healthcare products; low accuracy of pattern recognition algorithms in image processing system may result incorrect medical diagnosis.

(Case-4.5) Access control problem causing dangerous errors in information system; a malicious agent may enter false data into HIS during the absence of authorized users.

(Case-4.6 : Testing) Swap or mixing of test data of various patients or drugs administration due to confusion, poor document management, lack of clear understanding or training of the healthcare workforce; false data injection on viruses in test reports are serious threats in today's healthcare practice. The patients are not often given test reports today by the service provider to hide malicious trading practice or to charge extra amount. Testing of uncommon viruses (e.g. Dengu) enhance the cost of testing unnecessarily. Sometimes, broadcast of epidemic results panic among the public and this critical and helpless situation is exploited by malicious testing and medicare practice inhumanly.

(Case-4.7) Errors in decision making by the health consultants due to lack of proper knowledge management system (e.g. case based reasoning, intelligent DSS and GDSS) or misperception or lack of coordination among the workforce of various
departments or inappropriate enterprise application integration or error in test reports; incomplete prescription due to memory failure or silly mistakes.

(Case-4.8) Errors in scheduling due to exceptions (e.g. unfit patients, non-availability of healthcare experts), flawed and inadequate doctor-patient ratio;

(Case-4.9) surgical operation by unauthorized and unskilled workforce, intentional errors due to malicious business practice, lack of ethics, casual approach and dull HR policy; unintentional errors due to physical and mental fatigue for excessive workload and sickness, nonavailability of basic infrastructure and logistics arrangements;

(Case-4.10) Lack of verification of correctness of computation in medical billing and payment processing by the service provider and / or service consumer;

(Case-4.11) Incorrect data in patient's help guide may cause confusions and mismatch between the computed results and perceived one;

(Case-4.12) Incorrect feedback by the patients or their attendants due to misperception, misunderstanding of feedback form, lack of knowledge and critical observations or casual attitude.

A malicious agent may launch attacks on TPS, DSS, GDSS, KMS and BIS through malicious data mining, insecure data storage, flaws in data visualization and image processing algorithms and transaction processing logic. A secure service oriented computing model must address correct identification, authentication, authorization, privacy and audit for each e-transaction. For any secure service, the system should ask the identity and authentication of one or more agents involved in a communication. The agents of the same trust zone may skip authentication but it is essential for all sensitive communication across different trust boundaries. After the identification and authentication, a service should address the issue of authorization. The system should be configured in such a way that an unauthorized agent cannot perform any task out of his scope. The system should ask the credentials of the requester; validate his credentials and authorize the user to perform a specific task. Each trading agent should be assigned an explicit set of access rights according to his role. Privacy is another important issue. A trading agent can view only the information according to his authorized access rights. Finally, the system should audit each transaction - what has happened after the execution of a specific service.

Today, healthcare faces critical legal, ethical and psychological issues from the perspectives of security, privacy, confidentiality and organizational policy. Security and privacy of data is important from the perspectives of access control, data storage, version control of critical applications, accountability, traceability and transparency. E-healthcare information should be managed in a digital environment through efficient security principles, privacy laws and policies in the domain of shared and managed care. Shared care is a healthcare service that is delivered at multiple locations and by multiple service providers through sharing of the medical information of the patients. Managed care is characterized by cost reduction and quality enhancement techniques practiced by either healthcare service providers or insurance companies. Both paradigms require secure exchange of patient's private data through internet. Another important issue is effective implementation of
Hospital Protection Act in the event of the death of the patients and the arrest prevention act of healthcare professionals.

**Theorem 5: Malicious business intelligence is a critical threat factor of bio-terrorism. The conflict between security intelligence and business intelligence is inevitable. It needs fair, rational and intelligent business model innovation.**

Malicious business intelligence attacks a life-science supply chain and healthcare service chain through greedy heuristics in payment function for revenue and profit optimization, economic pressure and incentive policy, fraudulent health insurance model, flaws in investment decision on technology management, irrational and dull HR policy in talent management and chaotics in formulation of public policy, mechanisms and corporate governance. In fact, the conflict between business intelligence and security intelligence is inevitable. It is a disguised form of capital punishment. The properties of secure multi-party computation are applicable to resolve this conflict between security and business intelligence.

(Case-5.1: *Pressure on profit and revenue target*) : A critical patient Alice is treated in intensive care unit unnecessarily for long duration even there is no hope of recovery. AC charge is billed even after her death. There is another instance of a patient Bob having a simple medical problem. But, he is prescribed costly medical drugs and is treated through different costly testing procedures (e.g. CT Scan, USG, MRI scan) unnecessarily to enhance the cost of treatment. The basic objective is to maximize the revenue from medical bills. There are various models: A healthcare consultant takes high service charge; correctly diagnose and recommend optimal drugs and testing procedure. The consulting charge is high, but the treatment cost is reasonable. In another model, the consulting charge is minimal, but the cost of treatment such as the cost of testing and medicine is very high. Which is really a good model?

(Case-5.2 : *Fraudulent Health Insurance Products*) : This is the case of a people-friedly affordable health insurance model. Health is wealth. But, life is full of uncertainties. The cost of healthcare is rising but the earning is getting low. In this situation, the people are forced to drain all their savings to pay hospital bills. They can get high quality of healthcare service and tax benefits through a set of intelligent health insurance plans.

A health insurance plan should cover hospitalization expenses including consulting testing and surgical operations, ambulance, room rent, boarding and nursing expenses. Thus, the common people can avail financial security, high quality of healthcare service and tax benefits. But, they should be rational in buying appropriate health insurance plans considering various terms and conditions, scope, needs and budget. Generally, health insurance is a yearly policy which should be renewed each year subject to certain exclusions and waiting period. Health insurance can be claimed in two ways: reimbursement and cashless. In case of reimbursement, a person gets admitted in a hospital, pays hospital bills, submit the documents and claim form to the insurance service provider for reimbursement. On the other side, a health insurance service provider may have a network of hospitals
with specific arrangement for direct billing. An insurance person is admitted in a
network hospital by submitting a cashless card. The hospital processes cashless
admission of the patient and hospital expenses are paid by the insurance service
provider covered by the plan.

The financial intelligence of healthcare service is associated with multiple payment processing options such as health insurance, credit card, direct cash payment, bank loan and donation. Another important issue is discriminatory pricing of drugs, biomedical devices, healthcare and surgical products and services and also combinatorial pricing plan for different packages. The basic building block of digital transformation is a well-designed web application schema. The content of the web portal should be defined intelligently and is expected to provide a transparent overview of various types of health insurance plans, their benefits and limitations, business rules, group buying strategy, claim processes (reimbursement and cashless), payment processing options and discriminatory singular or combinatorial pricing strategy. An online premium calculator should be able to compute premium and service tax for a selected health insurance product, age band and sum insured with a comprehensive cost-benefit analysis, risk assessment and mitigation strategies and tax savings. The service consumers and health insurance service providers should be able to process buy, renew, claim and reimburse functions online maintaining privacy, fairness, correctness, trust, transparency and rational information exchange. Additionally, the web enabled application should provide detailed information on help guide, buying tips, free helpline number, mobile commerce model, customer service contact number and e-mail id, legal and regulatory compliance issues correctly. The payment function should be designed innovatively, fairly and rationally in terms of intelligent contract, pricing strategy, payment terms, incentives and penalty function. It is essential to audit malicious business intelligence of a health insurance model by verifying transparency and accountability of the payment mechanism from the perspectives of violation in contractual clauses among the agents, flaws in payment function computation or pricing algorithm and package configuration and commitment.

(Case-5.3: Malicious HR Policy for Talent Management) For effective healthcare system innovization, digital technology management is not only the critical success factor. There are other several factors such as HR policy in talent management, quality of medical education, intelligent public policy, mechanisms and corporate governance. The healthcare consultants, specialists and work force need a good human resource management model for proper talent acquisition and retention, research and innovation, career growth planning, incentive, reward, recognition and retirement planning. The healthcare service provider may have a flawed business model based on old legacy information technology, malicious healthcare practice due to economic and financial pressure, mechanical HR policy and bad resource allocation mechanism. The patients or service consumers may lose trust in health care products and practice due to costly treatment procedure, complicated and fraudulent business rules and vague computational intelligence. Such an inefficient system increases the risk of bio-terrorism through malicious behavior of the trading agents, administrative inefficiencies, collusion of the trading agents
against regulatory compliance, financial fraud in e-transactions, quality problems in
testing and sourcing, non-availability, poor performance and failure of medical
equipments, malicious work culture, medical negligence, unauthorized absence,
excessive work load, strikes and physical security problem of healthcare service
provider. The healthcare workforce expect to work freely in a collaborative, flexible
and ethical work culture without any financial, physical, mental and cultural
constraints and pressures.

Globally healthcare organizations are undertaking massive business process
reengineering initiatives and many of these reforms are supported by the strategic
use of advanced information and communication technology. It should provide
better integration and improved coordination of flows of material, information and
funds within and across healthcare firms, experts and patients. This results
improved patient care, greater accuracy, cost efficiency, ease of processing,
increased productivity and fast response time in healthcare service. Service oriented
computing results improved interoperability, increased federation, and
organizational agility through a standardized, flexible, reliable and scalable
architecture. However, it enhances the risk of various types of malicious attacks on
the healthcare service chain and life-science supply chain and these are the
emerging threats of bio-terrorism today.

Cryptographic challenges

Theorem : It is essential to develop new cryptographic tools and techniques to satisfy multi-dimensional properties of secure multi-party computation for ensuring security intelligence of a complex system at improved cost of computation and communication.

Traditionally, the concept of encryption and decryption has been used to preserve
the privacy of critical data for an intelligent healthcare information system. It is
used for private communication and secure data storage. Intelligent transaction
processing systems have used cryptographic techniques effectively to ensure
authentication, authorization, access control, correct identification, privacy and
audit of electronic transactions. The most critical issue is the cost of computation of
security algorithms. In the context of secure communication, cryptography ensures
privacy and secrecy of sensitive data through encryption method. S encrypts a
message (m) with encryption key and sends the cipher text (c) to the recipients (R).
R transforms c into m by decryption using secret decryption key. An adversary may
get c but cannot derive any information. R should be able to check whether m is
modified during transmission. R should be able to verify the origin of m. S should
not be able to deny the communication of m. There are two types of key based
algorithms: symmetric and public key. Symmetric key encryption scheme provides
secure communication for a pair of communication partners; the sender and the
receiver agree on a key k which should be kept secret. In most cases, the encryption
and decryption keys are same. Secure authentication is hard with symmetric
encryption key with untrusted recipients. In case of asymmetric or public-key
algorithms, the key used for encryption (public key) is different from the key used
for decryption (private key). The decryption key cannot be calculated from the
encryption key at least in any reasonable amount of time. Asymmetric RSA encryption achieves authentication where each recipient can verify the authenticity of received data but can not generate authentic messages.

A digital signature is a cryptographic primitive by which a sender (S) can electronically sign a message and the receiver (R) can verify the signature electronically [36]. S informs his public key to R and owns a private key. S signs a message with its private key. R uses the public key of S to prove that the message is signed by S. The digital signature can verify the authenticity of S as the sender of the message. A digital signature needs a public key system. A cryptosystem uses the private and public key of R. But, a digital signature uses the private and public key of S. A digital signature scheme consists of various attributes such as a plaintext message space, a signature space, a signing key space, an efficient key generation algorithm, an efficient signing algorithm and an efficient verification algorithm. Digital signature provides authentication and non-repudiation through asymmetric property of cryptography at high cost of computation and communication. One way hash function may be used as the basic building block of asymmetric RSA digital signature and cryptographic commitment. A one-way function is a function that is easy to compute but computationally infeasible to invert. If x is a random string of length k bits and F is a one-way function then F can be computed in polynomial time as $y = F(x)$ but it is almost always computationally infeasible to find $x'$ such that $F(x') = y$. Merkle hash tree is an efficient construction of one way function.

Another alternative interesting option for secure authentication is *signcryption*. Traditional signature-then-encryption is a two step approach. At the sending end, the sender signs the message using a digital signature and then encrypts the message. The receiver decrypts the cipher text and verifies the signature. The cost for delivering a message is the sum of the cost of digital signature and the cost of encryption. Signcryption is a public key primitive that fulfills the functions of digital signature and public key encryption in a logically single step and the cost of delivering a signcrypted message is significantly less than the cost of signature-then-encryption approach. A system is vulnerable to insecure communication. The basic objective is that the system properly signciphers all sensitive data. A pair of polynomial time algorithms (S,U) are involved in signcryption scheme where S is called signcryption algorithm and U is unsigncryption algorithm. The algorithm S signciphers a message m and outputs a signcrypted text c. The algorithm U unsignciphers c and recovers the message unambiguously. (S,U) fulfill simultaneously the properties of a secure encryption scheme and a digital signature scheme in terms of confidentiality, unforgeability and non-repudiation. Signcryption can ensure efficient secure communication. In a triplet Elgamal signature scheme [r,e,s], the commitment r is computed as $r = g^k[\text{mod } p]$ where g and p are part of the public key and the commitment k is an integer independent to such values [15]. The signature generation scheme permits the receiver to recover the commitment by computing $r = g^s y^e(\text{mod } p)$. The sender computes the commitment in such a way that it is only recoverable by the receiver. The commitment value can be used as a symmetric key shared between the sender and the receiver and this symmetric encryption provides message confidentiality. The recoverable commitment value of Elgamal triplet
signature scheme is used as the symmetric key to achieve symmetric encryption of
the message while the triplet signature serves the signature.

Alternatively, the agents may adopt different types of privacy preserving data
mining (PPDM) strategies such as randomization, summarization, aggregation,
generalization, suppression, de-identification and k-anonymity. Intelligent PPDM
strategies may improve the cost of computation in secure communication. The basic
objective is to provide confidentiality, data integrity, authentication and non-
repudiation in the communication of sensitive data.

Let us now conclude this work. The threat analytics clearly shows cryptographic
challenges of secure multi-party computation for intelligent and complex systems
and critical applications. It is essential to develop new cryptographic techniques and
tools which should be able to provide collective security intelligence in terms of
correctness, fairness, rationality, trust, transparency, accountability, reliability,
confidentiality, data integrity, non-repudiation, authentication, authorization,
correct identification, privacy, safety and audit of complex information systems used
in healthcare service chain and life-science supply chain. It is essential to detect
malicious attacks on the information system and communication schema
intelligently in time through efficient model checking and system verification
mechanisms. Another critical agenda is to improve the cost of computation and
communication of secure multi-party computation protocols. Efficient signcryption
and unsigncryption algorithms should be implemented practically in the form of
information security solutions. The properties of secure multi-party computation
are useful to resolve the conflicts between the security intelligence and business
intelligence of a complex mechanism. It is an interesting option to explore the
application of secure multi-party computation for the design of intelligent
mechanisms through the cross fertilization of cryptography and algorithmic game
theory. Is it possible to generate signcrypted e-prescription by healthcare
consultants?

6. STRATEGY

Strategy Analytics

Agents: system analysts, business analysts, scientist, engineers, technology management consultants;

Strategic moves : Focus on emerging healthcare, life science, biotechnology, pharmaceutical technologies.

Call deep analytics ‘7-S’ model; explore how to ensure a perfect fit among 7- S elements – scope, system, structure, security, strategy, staff-resources, skill- style-support;

Define a set of security goals and emerging technologies accordingly.

* Do SWOT analysis: strength, weakness, opportunities and threats of existing technologies as compared to emerging technologies;

- Fair and rational business model innovation

- Who are the consumers?
* What should be the offering of products and services?

* What do the consumers value?

* How to deliver values to the consumers at rational cost?

Do technology life-cycle analysis on 'S' curve : presently at emergence phase of 'S' curve.

Explore technology innovation-adoption-diffusion strategy.

* Cancer prevention strategies

★ Proactive approach

★ Reactive approach

★ Deep learning

★ Intelligent reasoning

* Case based reasoning

* Perception common sense reasoning

* Regenerative medicine

* Precision medicine

* Cancer genomics

★ Biomedical technology

Explore innovation model and knowledge management system for creation, storage, sharing and application of knowledge.

Adopt ‘4E’ approach: envision, explore, exercise and extend.

Dr. Muller and Dr. Nil Harvey are analyzing the strategy of innovation, adoption and diffusion of emerging healthcare technologies; it can be analyzed from different dimensions such as proactive self-healing approach, reactive approach, deep learning algorithm, intelligent reasoning and biomedical instrumentation. Intelligent reasoning should be explored in terms of case based reasoning, perception and common sense, logical and analytical reasoning and rational payment function. It is essential to adopt a set of reactive strategies such as alternative, integrated, regenerative and precision medicines to fight against cancer. It is rational to evaluate strength, weakness, opportunities and threats for various strategic options. The evolution and diffusion of cancer prevention technology depends on R&D policy, organization learning, knowledge management strategy and technology life-cycle analysis. An intelligent R&D policy should be explored through shared vision, goal and strategic alliance, collaborative and collective intelligence. There are various strategies of learning such as learning-by-doing and learning-before-doing. Learning-by-doing is effective in cancer care through deep learning on big data; it is also essential to attain deep practical and theoretical knowledge on cancer therapy through experimental medicines. In fact, it is clear from the aforesaid case analysis that different types of cancer demand different types of prediction and prevention technologies, best practices and therapies.

*Technology trajectory* is the path that the cancer prevention technology takes through its life-cycle from the perspectives of rate of performance improvement, rate of diffusion and rate of adoption in cancer care. It is really complex to analyze the impact of various factors on the trajectory of cancer prevention technology today. From the view of life-cycle, the technology of cancer prevention is at the growth phase of S-curve. The technology has evolved from the birth phase and
going though growth phase. Initially, it may be difficult and costly to improve the
performance of the new cancer prevention technology. The performance is expected
to improve with better understanding of the fundamental principles and
mechanisms of human biological system. Initially, the technology may be costly for
the adopters due to various uncertainties and risks. Gradually, it is expected to be
adopted by large segments of the market due to reduced cost and risks. The
evolution of the technology is passing through a phase of turbulence and
uncertainty; various entities are exploring different competing options of cancer
care and a dominant therapy is expected to emerge through a consensus and
convergence of the best practices. The dominant therapy must consider an optimal
set of technological advancements which meet the demand of the cancer patients,
cancer care experts, supply chain and design chain in the best possible way.

Let us consider the strategy of surgical operation for cancer care. It is essential to operate hard and soft tissues with proper care, precision, consistency, speed and control. The surgical operation is expected to be minimally invasive, less painful and faster healing. An innovative technological solution and surgical method for cancer care is expected to have higher success rate; lower recurrence rate, more precision, accuracy and effectiveness, less treatment time, faster recovery and healing, unmatched cutting, speed and control with high consistency and reliability, greater precision and control, efficiency and safety, less post operative problems due to minimally invasive procedure preventing damage to nearby tissues and less bruising, numbness and post operative pain, minimal invasion and no scars, simple OPD procedure, better patient experience and thus high credibility and high patient satisfaction. Laser technology is an emerging solution for the aforesaid desired surgical operation in cancer care.

Laser (light amplification by stimulated emission of radiation) is an effective
solution as compared to conventional methods. Laser has a specific wavelength; it is
focused in a narrow beam and creates a very high intensity light which is used for
cutting through tissue in surgical operation. Laser therapy is used to destroy
tumors or precancerous growths in skin, cervical, penile, vaginal, vulvar and lung
cancer. It may be also used for cancer related to brain, prostate, piles, fissures,
fistula and dental problems. Laser therapy can be used in standalone mode or in
combination with chemotherapy or radiation therapy. It is used to relieve certain
symptoms of cancer such as to remove a tumor blocking trachea or esophagus or
colon polyps or stomach. It can also be used to seal nerve endings and to reduce pain
after surgical operation and seal lymph vessels to reduce swelling and limit the
spread of tumor cells. Laser therapy is given through a flexible endoscope fitted
with optical fiber. It is inserted through mouth, nose, anus or vagina. Laser is then
precisely focused to remove a tumor.

There are various types of laser therapies such as Laser-induced Interstitial
Thermotherapy (LITT) and Photodynamic Therapy (PDT). Generally, CO2, argon
and neodymium: yttrium-aluminum-garnet (Nd:YAG) lasers are used for cancer
care. Laser therapy provides several benefits such as minimally invasive procedure,
faster recovery, minimal recurrence, blood loss, pain and post operative discomfort
and high success rate. Laser therapy demands specialized training of the surgeons
and strict safety precautions. It is expensive; the effects may not last long and may
be repeated for recovery.

<figure><img src="image_38.png" /><figcaption>Figure 5.3 : Technology life-cycle analysis</figcaption></figure>

**Precision Medicine for Cancer Care** : Let us exercise SWOT analysis on *precision medicine*; it is a medical model having customization of healthcare practice in terms of medical decisions, treatments, practices, products and services being tailored to individual patient. Diagnostic testing is done for genetic, molecular and cellular analysis to select correct and optimal method of the treatment of a patient using a set of intelligent tools such as molecular diagnostics, imaging, and analytics. Precision medicine is basically tailoring of medical treatment to individual characteristics of each patient; not creation of drugs or medical devices unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease or in their response to a specific treatment. Preventive or therapeutic interventions can then be focused on those patients who will benefit considering a set of critical issues such as patient care cost and side effects of medicine. Genomic medicine is an emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g. for diagnostic or therapeutic decision making) and the health outcomes and policy implications of that clinical use. P4 is an approach to make medicine more predictive, preventive, personalised and participatory. The basic objectives are to quantify wellness and predict and prevent disease; understand human health and moderate the course of chronic diseases, correct disabling physical conditions and cure molecular deficiencies; it demands the convergence of system medicine, digital technologies and consumer driven healthcare. Personalized medicine is a form of medicine that uses information on genomics i.e. person's genes, proteins and environment to prevent, diagnose and treat disease.

Precision oncology is the branch of precision medicine for cancer care based on
molecular profiling tests and DNA sequencing. Artificial intelligence is providing a
paradigm shift towards precision medicine to understand genotypes and phenotypes
in existing diseases improve the quality of cost effective patient care and reduce
mortality rates. Machine learning algorithms are used for genomic sequence
analysis and drawing inferences using big data analytics. Health care service
providers are expected to understand better the impact of environment, lifestyle and
heredity on patient's health, disease or condition using the knowledge of precision
medicine, They can predict correctly which treatments will be most effective, preventive and safe, There are other several benefits of precision medicine : shift from reaction to prevention, predict susceptibility to disease accurately, improve detection of disease, preempt progression of disease, customize prevention strategies of disease, prescribe more effective drugs, avoid prescribing drugs with predictable side effects, reduce the time, cost, and failure rate of pharmaceutical clinical trials, eliminate trial and error inefficiencies that may inflate health care costs and undermine patient care. One of the critical issues in cancer patient care is the side effects of chemotherapy. Precision medicine may be an interesting strategic move to cure cancer in future.

**Cancer Genomics**: Cancer is considered as a *genomic* disease since a normal cell becomes a cancer cell through successive genomic alterations. Molecularly Targeted Agents (MTAs) block a peculiar molecular genomic alteration in cell proliferation, angiogenesis, metastasis and invasion of tumor. Precision medicine analyzes information on genes, proteins, environment, location of tumor and histology of a patient to diagnose, treat and prevent cancer. Precision medicine is an emerging technology in oncology based on the concept of MTAs. It is important to analyze the effectiveness of precision medicine in terms of efficiency of bioinformatics algorithms, histology independent drug development, clinical trials to evaluate a treatment algorithm instead of drug efficacy, molecular genomic alterations across different types of tumor and molecular profiling of tumor of cancer patients.
RNAs are polymeric molecules which carry genetic information and used in protein synthesis; only a minor fraction of human genomes encode for proteins and the remaining large fraction of the transcripts are noncoding RNAs (ncRNAs). The noncoding RNAs are potential biomarkers and therapeutic targets to facilitate precision medicine in cancer care. It is an interesting research agenda to explore diagnostic, prognostic, biomarkers and therapeutic strategies of ncRNAs. ncRNAs are broadly classified into distinct classes based on the lengths, unique biogenesis routes, three dimensional structure and modes of action such as ribosomal RNAs, transport RNAs, small nucleolar RNAs (snoRNAs), microRNAs (miRNAs), small interfering RNAs (siRNAs), PIWIinteracting RNAs (piRNAs), hairpin RNAs (hpRNAs) and long noncoding RNAs. ncRNAs regulate gene expression through diverse mechanisms such as mediating imprinting, alternative splicing and modification. Deep sequencing technologies generate high throughput transcriptomic and demand the support of efficient bioinformatics mechanisms for proper storage, intelligent analysis, visualization and interpretation of data, RNA identification, structure modeling, functional annotation and network inference. US government invested $215 million to launch Precision Medicine Initiative (PMI) in 2015. It is a customized healthcare model which considers individual variability and tailors medical treatment to individual patient. Precision medicine is dependent on molecular diagnostics to select correct therapies based on genetic data of a patient.
Genomic information is getting used for the diagnosis of lung, breast and pancreatic cancer. The treatment of lung cancer is effective based on diagnostic, prognostic and predictive findings, microRNA profiling and high throughput sequencing.
Molecular alterations and routine genomic profiling is getting applied for the
treatment of lung cancer. Evolving technologies (e.g. next generation sequencing, high density microarrays and high throughput expression profiling platforms) enable genomic profiling at a reasonable cost. Pancreatic Ductal Adenocarcinoma (PDAC) is a lethal disease with the worst prognosis among all solid tumors. Genomic landscape has revealed that massive scale sequencing provides unprecedented opportunity to dramatically improve diagnosis and treatment of pancreatic cancer. Breast cancer is the most frequent female cancer. Genomics is effectively used for the treatment of breast cancer through proper hormone therapy.

**Regenerative Medicine for Cancer Care**: The basic objective of regenerative medicine is to develop methods for regrowing, repairing or replacing damaged or diseased cells, organs or tissues (William Haseltine, 1999). It includes generation and use of therapeutic stem cells therapy, tissue engineering, molecular biology, cell transplantation, production of artificial organs and biomechanics prosthetics. It deals with the process of replacing, engineering or regenerating human or animal cells, tissues or organs to restore or establish normal function. It tries to engineer damaged tissues and organs by stimulating the body's own repair mechanisms to functionally heal previously irreparable tissues or organs. It includes the injection of stem cells obtained through directed differentiation (cell therapies); the induction of regeneration by biologically active molecules administered alone or as a secretion by infused cells (immunomodulation therapy); and transplantation of in vitro grown organs and tissues (tissue engineering). It is relatively a costly treatment. There are various types of regenerative medicine such as Stem cell therapy (Stem cells injected into bone, cartilage or fat cells to stimulate healing in the body), platelet rich plasma (prp), lipogems and prolotherapy. Stem cell injections may last upto one year. Stem cells may be effective in cancer care to repair, restore, replace and regenerate cells at the target location,

Regenerative medicine covers a range of treatments intended to repair or replace damaged cells, tissues, or organs such as cell therapies, bioengineered tissue products, and gene therapies. Stem cell treatment involves infusion of healthy stem cells into the patient through a painless process of stem cells transplantation through intravenous (IV) infusion. For bone marrow or blood stem cell transplant, engraftment takes 2-3 weeks; for cord blood transplant, it takes 3=5 weeks. Stem cell transplant has some negative side effects such as low blood cell counts, Graft-versus-host disease (GVHD), Veno-occlusive disease (VOD), digestive system problems, skin and hair problems, pain, kidney and lungs problem. The success rate is 82.2%. The basic objective of regenerative medicine is to provide safe and reliable ways to repair, restore or replace damaged tissues or organs. Cord blood and tissues are the building blocks of regenerative medicine. Natural stem cells extracted from embryonic, hematopoietic, mesenchymal or adult tissues) or induced progenitor stem (iPS) cells can be modified by gene therapy for use in regenerative medicine. Stem cell therapy is useful for regrowth of cartilage, knee pain and growth of hair. There are side effects such as temporary swelling and pain. Regenerative medicine is an interesting strategic option of cancer care in future.
*Integrated Medicine for Cancer Care: Integrative medicine* may be any type of medical practice or product that is not standard care. For cancer, standard care may be surgery, chemotherapy, radiation and biological therapy. Integrative medicine is complementary care used along with standard care. For cancer, it combines the best of both types of care. It is a combination of medical treatments for cancer and complementary therapies to cope with the symptoms and side effects. Complementary Alternative Medicine (CAM) may include acupuncture, yoga and meditation for treatment of cancer and scientific evidence supports this approach to health and healing. Can cancer be cured naturally? There is no evidence of alternatives of cancer cure. Alternative complementary therapies can interfere with chemotherapy or radiation and make them less effective or may have other negative effects. Integrative medicine combines complementary treatments with conventional care. Conventional medicine relies on methods proved to be safe and effective with carefully designed trials and research. But, many complementary and alternative treatments lack solid research for sound decisions. What are the benefits of integrative medicine? It is a comprehensive approach to care i.e. treating the patient through a holistic approach; not just the condition or disease; setting the foundation for overall health, This method may reduce the cost of cancer care. But, it may have negative effects or even risks of death. What is the best treatment for cancer? In fact, cancer care may be done effectively through a combination of treatments such as surgery with chemotherapy and/or radiation therapy, immunotherapy, targeted therapy or hormone therapy, precision medicine, regenerative medicine and integrated medicine.

Dr. Muller is analyzing the strategy of innovation, adoption and diffusion of biomedical technologies. This element can be analyzed from different dimensions such as R&D policy, learning curve, SWOT analysis, technology life-cycle analysis and knowledge management strategy. An intelligent R&D policy should be defined in terms of shared vision and goal. Biomedical innovations are closely associated with various strategies of organization learning and knowledge management. The aforesaid biomedical innovation is closely associated with R&D policy and organizational learning strategies in new product development. There are various strategies of learning such as learning by doing and learning before doing. Learning before doing is possible through laboratory experiments, prototype testing and simulation. Deep practical and theoretical knowledge can be achieved through laboratory experiments. Learning by doing is also important to understand the impact or side-effects of the implantation of biomedical devices and oral insulin.

Technology innovation on biomedical technology is associated with various strategic moves and functions such as scope analysis, requirements engineering, quality control, product design, concurrent engineering, talent management, team management and coordination, resources planning, defining products specification, concept development, concept evaluation, system architecture design, detailed design, production plan development, roll out, prototyping, testing, documentation, tools development and regulatory issues [37-38].

The design of a medical product is a complex task having uncertain information, high stakes and conflicts, varieties in size, scope, complexity, importance and cost and various constraints in terms of product quality, product cost, development cost,
development time and development capability. It is essential to adopt concurrent engineering approach which considers all aspects of the problems faced and a clearly defined role. It is challenging to develop a product development team with specific skills and group dynamics. The next step in our process is to transform these needs into specifications that can be used to guide the design decisions. These product specifications become the targets for the product development. The concept development phase is the time to search out ideas that will meet the need. Next important steps are concept evaluation through SWOT analysis, design of system architecture, prototyping and product testing. Finally, it is essential to satisfy various issues of regulatory compliance such as approval of FDA. It is not a simple task to make rational decisions on strategic technologies, ICT, technology infrastructure development and renewal of existing technologies.

**SWOT Analysis:** It is an interesting research agenda to analyze strength, weakness, opportunities and threats of innovation on biomedical technology. Strength indicates positive aspects and benefits; weakness shows negative aspects and limitations of the same; opportunities explore the growth potential and threats assess the risks of the technology. Let us compare two strategic options for the treatment of diabetes: oral insulin vs. artificial pancreas based on biomedical technology. The critical observation from this deep analytics is that oral insulin is a rational, simple, practically feasible and safe option as compared to artificial pancreas. But in case of pancreatic cancer, artificial pancreas may be an essential option. However, the concept of artificial pancreas is a very complex and costly strategic option and there is threat of immunity and safety from the perspectives of adaptability of human biological system. But, both the aforesaid options are not matured at their technology life-cycle; they are now at emergence phase. It is also essential to adopt a set of proactive and reactive measures to fight against diabetes such as herbal and homeopathic medicines, yoga, meditation, healthy life-style, obesity control and organ replacement.

Diabetes is a disorder of deficiency in insulin, a peptide hormone of pancreas. Insulin is generally given by subcutaneous (SC) route; the non-compliance of diabetic patients is a common occurrence. Oral insulin is an alternative option but it is essential to identify appropriate delivery mechanism. Oral insulin is the dream of diabetic patients. Nanotechnology may be an innovative strategic option in this connection due to the size of particles in nano range and greater surface area [3,4]. These physical and chemical properties improve the absorption of nanoparticles as compared to larger carriers. This is a real challenge of today's research on oral insulin from the academic and industrial community. Is it possible to use nanoparticles as a carrier to deliver insulin orally?

Let us analyze the strength and opportunities of oral insulin delivery mechanism which support a cost effective, convenient, simple and painless treatment; it reduces the risk of hypoglycemic incidents, immune responses and obesity. The threat and weaknesses of SC route mechanism may be considered from various aspects such as hyperinsulinemia, lipoatrophy, lipohypertrophy, patient noncompliance, painful procedure of injections and cost for the treatment for hyperglycemia, retinopathy, neuropathy and nephropathy. There are various types of Diabetes Mellitus such as Type I, Type II, gestational and secondary.
Now, let us consider the strength of nanomedicines. For oral insulin delivery, it is possible to explore various types of options such as nanoparticles (NPs), liposomes, microemulsions (MEs), self-nanoemulsifying drug delivery systems (SNEDDS), micelles, nanogels (NGs), microspheres, niosomes, and superporous hydrogels (SPHs). A NP is a small entity, particle size ranges from 10 to 1000 nm. Two major pathways by which NPs pass through intestinal epithelium are paracellular and transcellular. Transcellular route is the most common route of absorption. NPs can be classified into polymeric and lipid-based systems. Biocompatible and biodegradable polymeric NPs may be an ideal carrier for delivering proteins and peptides orally. It improves bioavailability of oral insulin. It may be Nanospheres and nanocapsules. Solid lipid nanoparticles (SLNs) offer some advantages like nano size range and comparatively narrow size distribution, controlled release of drug over a long period of time, protection of drug against chemical degradation, nontoxic, relatively cheaper and stable and can be easily freeze or spray dried. Liposomes offer several advantages such as nanosize, able to incorporate both hydrophilic and hydrophobic drug, improved effectiveness, better stability by encapsulation, non-hazardous, compatible in biological environment, biodegradable, and nonantigenic; biotinylated liposomes (BLPs) enhance the delivery of insulin.

Nanocarrier based systems for mucoadhesive drug delivery systems prevent degradation of entrapped drug and improve the circulation time of drug at absorption site. Polyionic polymers show mucoadhesive properties. From such polymers, alginate has shown the best candidate for the intestinal mucosal system. Alginate is a nontoxic, biodegradable, and mucoadhesive polysaccharide polymer that possesses mucoadhesive properties than carboxymethylcellulose, chitosan, poly (lactic acid), and other polyionic polymers.

It is essential to improve oral insulin delivery mechanism due to incomplete and unpredictable absorption through gastrointestinal tract, degradation due to varying pH of the stomach and enzymatic degradation lead to poor oral bioavailability. The structure of insulin is very delicate. Stability is affected by component and processing elements; the common degradation pathways are oxidation, photodegradation, disulfide scrambling, deamidation, aggregation, precipitation, dissociation and fragmentation.

Next let us focus on strength and weakness of artificial pancreas. Artificial pancreas is a technology that controls blood sugar level of diabetes patients and acts as the substitute of a healthy. A pancreas performs various exocrine digestive and endocrine hormonal functions There are alternative options of treatment such as insulin replacement therapy having life saving capability and manual control of blood sugar level with several limitations. The basic objectives of artificial pancreas is to improve insulin replacement therapy and to ease the burden of therapy. There are various types of approaches such insulin pump through close loop control based on real-time data from a continuous blood glucose sensor, bioengineering approach through surgical implantation of a biocompatible sheet of encapsulated beta cells and gene therapy approach through therapeutic infection by a genetically engineered virus which changes DNA of intestinal cells to insulin producing cells.