varunr14/newsummary huggingface dataset:
Breast cancer is the most commonly occurring type of cancer among women and it is the major cause of female cancer related deaths worldwide . Its incidence is increasing in developed as well as developing countries . Efficient strategies to reduce the high death rates due to breast cancer include early detection and tumor removal in the initial stages of the disease . Clinical and mammographic examinations are considered the best methods for detecting the early signs of breast cancer however these techniques are highly dependent on breast characteristics equipment quality and physician experience . Computer aided diagnosis systems have been developed to improve the accuracy of mammographic diagnosis usually such systems may involve three steps segmentation parameter extraction and selection of the segmented lesions and lesions classification . Literature considers the first step as the most important of them as it has a direct impact on the lesions characteristics that will be used in the further steps . In this study the original contribution is a microcalcification segmentation method based on the geodesic active contours technique associated with anisotropic texture filtering as well as the radiologists knowledge . Radiologists actively participate on the final step of the method selecting the final segmentation that allows elaborating an adequate diagnosis hypothesis with the segmented microcalcifications presented in a region of interest . The proposed method was assessed by employing 1000 ROIs extracted from images of the Digital Database for Screening Mammography . For the selected ROIs the rate of adequately segmented microcalcifications to establish a diagnosis hypothesis was at least 86.9 according to the radiologists . The quantitative test based on the area overlap measure yielded a mean of 0.52 0.20 for the segmented images when all 2136 segmented microcalcifications were considered . Moreover a statistical difference was observed between the AOM values for large and small microcalcifications . The proposed method had better or similar performance as compared to literature for microcalcifications with maximum diameters larger than 460 m. For smaller microcalcifications the performance was limited . 


Summary: Microcalcification segmentation method based on geodesic active contours. Method incorporates radiologists knowledge in its final stage. 86.9 of microcalcifications were adequately segmented. Performance satisfactory for microcalcifications larger than 460 m. Modest performance when segmenting microcalcifications smaller than 460 m.
Generalized quantum splines are piecewise polynomials whose generalized quantum derivatives agree up to some order at the joins . Just like classical and quantum splines generalized quantum splines admit a canonical basis with compact support the generalized quantum B splines . Here we study generalized quantum B spline bases and generalized quantum B spline curves using a very general variant of the blossom the generalized quantum blossom . Applying the generalized quantum blossom we develop algorithms and identities for generalized quantum B spline bases and generalized quantum B spline curves including generalized quantum variants of the de Boor algorithms for recursive evaluation and generalized quantum differentiation knot insertion procedures for converting from generalized quantum B spline to piecewise generalized quantum B zier form and a generalized quantum variant of Marsden s identity . 


Summary: Quantum splines are extended to splines whose divided differences agree at the joins. Generalized quantum blossoms are used to derive properties of generalized quantum splines. Algorithms and formulas are extended from quantum splines to generalized quantum splines. Generalized quantum B spline bases and generalized quantum B spline curves are also studied.
Background and objective Understanding the causes of disagreement among experts in clinical decision making has been a challenge for decades . In particular a high amount of variability exists in diagnosis of retinopathy of prematurity which is a disease affecting low birth weight infants and a major cause of childhood blindness . A possible cause of variability that has been mostly neglected in the literature is related to discrepancies in the sets of important features considered by different experts . In this paper we propose a methodology which makes use of machine learning techniques to understand the underlying causes of inter expert variability . Methods The experiments are carried out on a dataset consisting of 34 retinal images each with diagnoses provided by 22 independent experts . Feature selection techniques are applied to discover the most important features considered by a given expert . Those features selected by each expert are then compared to the features selected by other experts by applying similarity measures . Finally an automated diagnosis system is built in order to check if this approach can be helpful in solving the problem of understanding high inter rater variability . Results The experimental results reveal that some features are mostly selected by the feature selection methods regardless the considered expert . Moreover for pairs of experts with high percentage agreement among them the feature selection algorithms also select similar features . By using the relevant selected features the classification performance of the automatic system was improved or maintained . Conclusions The proposed methodology provides a handy framework to identify important features for experts and check whether the selected features reflect the pairwise agreements disagreements . These findings may lead to improved diagnostic accuracy and standardization among clinicians and pave the way for the application of this methodology to other problems which present inter expert variability . 


Summary: Inter expert variability in clinical decision making is an important problem. Retinopathy of prematurity is a disease that suffers from inter expert variability. We propose a methodology for understanding the causes of disagreement. The methodology provides a framework to identify important features for experts. An automatic system was also developed to deal with this problem.
The tri linear PLS2 iterative procedure an algorithm pertaining to the NIPALS framework is considered . It was previously proposed as a first stage to estimate parameters of the multi way PLS regression method . It is shown that the tri linear PLS2 procedure is convergent . The procedure generates a sequence of parameters which can be described as increasing or decreasing two specific criteria . Furthermore a hidden tensor is described allowing tri linear PLS2 to search its best rank one approximation . This tensor highlights the link between multi way PLS regression and the well known PARAFAC model . The parameters of the multi way PLS regression method can be computed using three alternative procedures . 


Summary: It is shown that the tri linear PLS2 procedure is convergent. The sequences generated by the tri linear PLS2 can be described as increasing or decreasing two specific criteria. A hidden tensor is described allowing tri linear PLS2 to search its best rank one approximation. A link between multi way PLS regression and the well known PARAFAC model is highlighted.
Objective Many regional programs of the countries educate asthmatic children and their families to manage healthcare data . This study aims to establish a Web based self management system eAsthmaCare to promote the electronic healthcare services for the asthmatic children in Taiwan . The platform can perform real time online functionality based upon a five tier infrastructure with mutually supportive components to acquire asthma diaries quality of life assessments and health educations . Methods We have designed five multi disciplinary portions on the interactive interface functioned with the analytical diagrams online asthma diary remote asthma assessment instantaneous asthma alert diagrammatical clinic support and asthma health education . The Internet based asthma diary and assessment program was developed for patients to process self management healthcare at home . In addition the online analytical charts can help healthcare professionals to evaluate multi domain health information of patients immediately . Results eAsthmaCare was developed by Java Servlet JSP technology upon Apache Tomcat web server and Oracle database . Forty one voluntary asthmatic children were intervened to examine the proposed system . Seven domains of satisfiability assessment by using the system were applied for approving the development . The average scores were scaled in the acceptable range for each domain to ensure feasibility of the proposed system . Conclusion The study revealed the details of system infrastructure and developed functions that can help asthmatic children in self management for healthcare to enhance communications between patients and hospital professionals . 


Summary: Five tier infrastructure with mutually supportive components on e Healthcare system. Five multi disciplinary portions on interactive interface with online diagrams. Internet based asthma diary and remote QOL assessment for self management at home. Online diagrammatical clinic support for evaluating real time health information. Instantaneous asthma alert and health education for healthcare improvement.
Heat Shock Proteins are the substantial ingredients for cell growth and viability which are found in all living organisms . HSPs manage the process of folding and unfolding of proteins the quality of newly synthesized proteins and protecting cellular homeostatic processes from environmental stress . On the basis of functionality HSPs are categorized into six major families namely HSP20 or sHSP HSP40 or J proteins types HSP60 or GroEL ES HSP70 HSP90 and HSP100 . Identification of HSPs family and sub family through conventional approaches is expensive and laborious . It is therefore highly desired to establish an automatic robust and accurate computational method for prediction of HSPs quickly and reliably . Regard a computational model is developed for the prediction of HSPs family . In this model protein sequences are formulated using three discrete methods namely Split Amino Acid Composition Pseudo Amino Acid Composition and Dipeptide Composition . Several learning algorithms are utilized to choice the best one for high throughput computational model . Leave one out test is applied to assess the performance of the proposed model . The empirical results showed that support vector machine achieved quite promising results using Dipeptide Composition feature space . The predicted outcomes of proposed model are 90.7 accuracy for HSPs dataset and 97.04 accuracy for J protein types which are higher than existing methods in the literature so far . 


Summary: We develop a predictor for classification of Heat Shock Proteins and J proteins. It is the combination of Dipeptide Composition and SVM. Two datasets were evaluated using jackknife test. Best results are reported so far in the literature.
Background and objective Insulin bolus calculators are simple decision support software tools incorporated in most commercially available insulin pumps and some capillary blood glucose meters . Although their clinical benefit has been demonstrated their utilisation has not been widespread and their performance remains suboptimal mainly because of their lack of flexibility and adaptability . One of the difficulties that people with diabetes clinicians and carers face when using bolus calculators is having to set parameters and adjust them on a regular basis according to changes in insulin requirements . In this work we propose a novel method that aims to automatically adjust the parameters of a bolus calculator . Periodic usage of a continuous glucose monitoring device is required for this purpose . Methods To test the proposed method an in silico evaluation under real life conditions was carried out using the FDA accepted Type 1 diabetes mellitus UVa Padova simulator . Since the T1DM simulator does not incorporate intra subject variability and uncertainty a set of modifications were introduced to emulate them . Ten adult and ten adolescent virtual subjects were assessed over a 3 month scenario with realistic meal variability . The glycaemic metrics mean blood glucose percentage time in target percentage time in hypoglycaemia risk index low blood glucose index and blood glucose standard deviation were employed for evaluation purposes . A t test statistical analysis was carried out to evaluate the benefit of the presented algorithm against a bolus calculator without automatic adjustment . Results The proposed method statistically improved all glycemic metrics evaluating hypoglycaemia on both virtual cohorts percentage time in hypoglycaemia and low blood glucose index . A statistically significant improvement was also observed on the blood glucose standard deviation . Apart from a small increase in mean blood glucose on the adult cohort the rest of the evaluated metrics despite showing an improvement trend did not experience a statistically significant change . Conclusions A novel method for automatically adjusting the parameters of a bolus calculator has the potential to improve glycemic control in T1DM diabetes management . 


Summary: A novel method that aims to automatically adjust the parameters of a bolus calculator is presented. A novel in silico evaluation under realistic intra day variability was carried out. The proposed method has the potential to significantly improve glycemic control in diabetes.
Cross validation methodologies have been widely used as a means of selecting tuning parameters in nonparametric statistical problems . In this paper we focus on a new method for improving the reliability of cross validation . We implement this method in the context of the kernel density estimator where one needs to select the bandwidth parameter so as to minimize risk . This method is a two stage subsampling extrapolation bandwidth selection procedure which is realized by first evaluating the risk at a fictional sample size sample size and then extrapolating the optimal bandwidth from to . This two stage method can dramatically reduce the variability of the conventional unbiased cross validation bandwidth selector . This simple first order extrapolation estimator is equivalent to the rescaled bagging CV bandwidth selector in Hall and Robinson if one sets the bootstrap size equal to the fictional sample size . However our simplified expression for the risk estimator enables us to compute the aggregated risk without any bootstrapping . Furthermore we developed a second order extrapolation technique as an extension designed to improve the approximation of the true optimal bandwidth . To select the optimal choice of the fictional size given a sample of size we propose a nested cross validation methodology . Based on simulation study the proposed new methods show promising performance across a wide selection of distributions . In addition we also investigated the asymptotic properties of the proposed bandwidth selectors . 


Summary: A two stage subsampling extrapolation bandwidth selection procedure is proposed. An automatic nested cross validation method is developed to select the subsample size. The extrapolated bandwidth selectors achieve a smaller mean square error. The second order extrapolated bandwidth selector has a relative convergence rate
Neuropsychological assessment tests have an important role in early detection of dementia . Therefore we designed and implemented a test battery for mobile devices that can be used for mobile cognitive screening . This battery consists of 33 questions from 14 type of tests for the assessment of 8 different cognitive functions Arithmetic orientation abstraction attention memory language visual and executive functions . This test battery is implemented as an application for mobile devices that operates on Android OS . In order to validate the effectiveness of the neuropsychological test battery it was applied on a group of 23 elderly persons . Within this group 9 were healthy and 14 were already diagnosed with dementia . The education level of the control group and dementia group were comparable as they spent 13.66 5.07 and 13.71 4.14 years at school respectively . For comparison a validated paper and pencil test was applied along with the proposed MCS battery . The proposed test was able to differentiate the individuals in the control and dementia groups for executive visual memory attention orientation functions with statistical significance . Results of the remaining functions language abstraction and arithmetic were statistically insignificant . The results of MCS and MoCA were compared and the scores of individuals from these tests were correlated . 


Summary: A novel mobile cognitive screening MCS test is designed for mobile devices. MCS has easier interface for elderly participants compared to the personal computers. MCS can offer a wider range of multimedia components compared to the paper tests. MCS can compare cognitive functions against the average of healthy individuals . MCS can differentiate healthy and impaired cognitive abilities.
We provide a continuation of the existing Activity Table Modeling methodology with a modular spreadsheets simulation . The simulation model developed is comprised of 28 modeling elements for the abdominal surgery cycle process . The simulation of a two week patient flow in an abdominal clinic with 75 beds demonstrates the applicability of the methodology . The simulation does not include macros thus programming experience is not essential for replication or upgrading the model . Unlike the existing methods the proposed solution employs a modular approach for modeling the activities that ensures better readability the possibility of easily upgrading the model with other activities and its easy extension and connectives with other similar models . We propose a first in first served approach for simulation of servicing multiple patients . The uncertain time duration of the activities is modeled using the function rand . The patients movements from one activity to the next one is tracked with nested if functions thus allowing easy re creation of the process without the need of complex programming . 


Summary: A framework for an abdominal surgery process simulation is proposed. The framework uses Activity table modelling technique and spreadsheets. The framework gives the state of each process activity at any point of time. It is easy read possibly to upgrading and extend with other activities and models.
In the paper two new approaches for construction of parametric polynomial approximants of a unit circle are presented . The obtained approximants have better approximation properties than those given by other methods i.e . smaller radial error symmetry and exponential error decay . 


Summary: Two new approaches for construction of parametric polynomial approximants of a unit circle are presented. The approximants have good approximation properties small radial error symmetry exponential error decay.
The cure fraction models have been widely used to analyze survival data in which a proportion of the individuals is not susceptible to the event of interest . In this article we introduce a bivariate model for survival data with a cure fraction based on the three parameter generalized Lindley distribution . The joint distribution of the survival times is obtained by using copula functions . We consider three types of copula function models the Farlie Gumbel Morgenstern Clayton and Gumbel Barnett copulas . The model is implemented under a Bayesian framework where the parameter estimation is based on Markov Chain Monte Carlo techniques . To illustrate the utility of the model we consider an application to a real data set related to an invasive cervical cancer study . 


Summary: We introduce a cure fraction model based on the generalized Lindley distribution. The joint distribution of the survival times was obtained by using copula functions. Parameters were estimated by Bayesian inference using MCMC simulation.
Multiple testing problems have received much attention . Different strategies have been considered in order to deal with this problem . The false discovery rate is probably the most studied criterion . On the other hand the sequential goodness of fit is a recently proposed approach . Most of the developed procedures are based on the independence among the involved tests however in spite of being a reasonable proviso in some frameworks independence is not realistic for a number of practical cases . Therefore one of the main problems in order to develop appropriate methods is precisely the effect of the dependence among the different tests on decisions making . The consequences of the correlation on the z values distribution in the general multitesting problem are explored . Some different algorithms are provided in order to approximate the distribution of the expected rejection proportions . The performance of the proposed methods is evaluated in a simulation study in which for comparison purposes the Benjamini and Hochberg method to control the FDR the Lehmann and Romano procedure to control the tail probability of the proportion of false positives and the Beta Binomial SGoF procedure are considered . Three different dependence structures are considered . As usual for a better understanding of the problem several practical cases are also studied . 


Summary: This paper studies the impact of correlation on the multitesting problem. An approximation for the distribution of the proportion rejections is provided. This approximation is used to develop multitesting adjusting procedures. The obtained results suggest that the proposed methodology gets a good compromise between Type I and Type II errors.
Identifying the abnormal changes of mental workload over time is quite crucial for preventing the accidents due to cognitive overload and inattention of human operators in safety critical human machine systems . It is known that various neuroimaging technologies can be used to identify the MWL variations . In order to classify MWL into a few discrete levels using representative MWL indicators and small sized training samples a novel EEG based approach by combining locally linear embedding support vector clustering and support vector data description techniques is proposed and evaluated by using the experimentally measured data . The MWL indicators from different cortical regions are first elicited by using the LLE technique . Then the SVC approach is used to find the clusters of these MWL indicators and thereby to detect MWL variations . It is shown that the clusters can be interpreted as the binary class MWL . Furthermore a trained binary SVDD classifier is shown to be capable of detecting slight variations of those indicators . By combining the two schemes a SVC SVDD framework is proposed where the clear cut cluster is detected by SVC first and then a subsequent SVDD model is utilized to divide the overlapped cluster into two classes . Finally three class MWL levels can be identified automatically . The experimental data analysis results are compared with those of several existing methods . It has been demonstrated that the proposed framework can lead to acceptable computational accuracy and has the advantages of both unsupervised and supervised training strategies . 


Summary: Recognition of mental workload is crucial to improve the safety of human machine system. A simulated process control task was used to elicit different levels of mental workload. Mental workload was assessed by using multichannel EEG signals. Locally linear embedding was used to reduce the dimensionality of EEG power features. Support vector clustering and support vector data description methods were combined to classify EEG data.