varunr14/newsummary huggingface dataset:
Background Evidence based medicine practice requires practitioners to obtain the best available medical evidence and appraise the quality of the evidence when making clinical decisions . Primarily due to the plethora of electronically available data from the medical literature the manual appraisal of the quality of evidence is a time consuming process . We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point of care . Methods Our approach extracts relevant information from medical article abstracts and utilises data from a specialised corpus to apply supervised machine learning for the prediction of the quality grades . Following an in depth analysis of the usefulness of features they are extracted from the text via rule based approaches and from the meta data associated with the articles and then applied in the supervised classification model . We propose the use of a highly scalable and portable approach using a sequence of high precision classifiers and introduce a simple evaluation metric called average error distance that simplifies the comparison of systems . We also perform elaborate human evaluations to compare the performance of our system against human judgments . Results We test and evaluate our approaches on a publicly available specialised annotated corpus containing 1132 evidence based recommendations . Our rule based approach performs exceptionally well at the automatic extraction of publication types of articles with F scores of up to 0.99 for high quality publication types . For evidence quality classification our approach obtains an accuracy of 63.84 and an AED of 0.271 . The human evaluations show that the performance of our system in terms of AED and accuracy is comparable to the performance of humans on the same data . Conclusions The experiments suggest that our structured text classification framework achieves evaluation results comparable to those of human performance . Our overall classification approach and evaluation technique are also highly portable and can be used for various evidence grading scales . 


Summary: We present a classification model for the automatic quality grading of clinical evidence. We propose NLP based approaches for extraction of informative features from text. We present a supervised learning approach using SVM classifiers for evidence grading. We show that the performance of our approach is comparable to human performance. Our quality grading approach can significantly reduce practitioners time needs.
Objective Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions . Despite the large availability of protein protein interaction information not much information is available about protein complexes . Experimental methods are limited in terms of time efficiency cost and performance constraints . Existing computational methods have provided encouraging preliminary results but they phase certain disadvantages as they require parameter tuning some of them can not handle weighted PPI data and others do not allow a protein to participate in more than one protein complex . In the present paper we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs . Methods and materials The proposed methodology is called evolutionary enhanced Markov clustering and it is a hybrid combination of an adaptive evolutionary algorithm and a state of the art clustering algorithm named enhanced Markov clustering . EE MC was compared with state of the art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms . Results Using public available datasets EE MC outperformed existing methodologies . Moreover when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity . In specific 5737 protein complexes were predicted and 72.58 of them are enriched for at least one gene ontology function term . Conclusions EE MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning . This fact was experimentally validated and moreover new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques . 


Summary: EE MC is a new unsupervised methodology for predicting protein complexes from weighted PPI graphs. It is by design able to overcome intrinsic limitations of existing methodologies. It outperformed existing methodologies increasing the separation metric by 10 20 . 72.58 of the predicted protein complexes in human are enriched for at least one GO function term.
Introduction The length of stay of critically ill patients in the intensive care unit is an indication of patient ICU resource usage and varies considerably . Planning of postoperative ICU admissions is important as ICUs often have no nonoccupied beds available . Problem statement Estimation of the ICU bed availability for the next coming days is entirely based on clinical judgement by intensivists and therefore too inaccurate . For this reason predictive models have much potential for improving planning for ICU patient admission . Objective Our goal is to develop and optimize models for patient survival and ICU length of stay based on monitored ICU patient data . Furthermore these models are compared on their use of sequential organ failure scores as well as underlying raw data as input features . Methodology Different machine learning techniques are trained using a 14 480 patient dataset both on SOFA scores as well as their underlying raw data values from the first five days after admission in order to predict the patient LOS and the patient mortality . Furthermore to help physicians in assessing the prediction credibility a probabilistic model is tailored to the output of our best performing model assigning a belief to each patient status prediction . A two by two grid is built using the classification outputs of the mortality and prolonged stay predictors to improve the patient LOS regression models . Results For predicting patient mortality and a prolonged stay the best performing model is a support vector machine with G A D 65.9 of 0.77 and G S L 73.2 . In terms of LOS regression the best performing model is support vector regression achieving a mean absolute error of 1.79 days and a median absolute error of 1.22 days for those patients surviving a nonprolonged stay . Conclusion Using a classification grid based on the predicted patient mortality and prolonged stay allows more accurate modeling of the patient LOS . The detailed models allow to support the decisions made by physicians in an ICU setting . 


Summary: A dataset of 14 480 critically ill patients within the ICU was collected. Machine learning models are constructed to predict patient length of stay and mortality. Prediction accuracy was improved by using a two by two classification grid. Probabilistic model outputs can improve interpretation by physicians. Moving data window shows potential for real time ICU patient analysis.
Context awareness enables the personalization of computer systems according to the users needs and their particular situation at a given time . The personalization capabilities are usually implemented by programmers due to the complex processes that are involved . However an important trend in software development is that more and more software systems are being implemented not only by programmers but also by people with expertise in other domains . Since most of the existing context aware development toolkits are designed for programmers non technical users can not develop these kinds of systems . The design of tools to create context aware systems by users that do not have programming skills but are experts in the domain where the system is going to be deployed will contribute to speed up the adoption of these kinds of services by the society . This paper presents a cloud based platform to ease the development of context aware mobile applications by people without programming skills . The platform has been designed to be used in a tourism domain . This way tourism experts can send tourist information to mobile users according to their context data . An energy efficient mobile app has been developed in order to obtain context data from the user s device and to receive personalized information in real time based on these data . The architecture and implementation details of the system are presented and the evaluation of the platform by tourism domain experts is discussed . 


Summary: We implement a cloud based platform to obtain and manage context data The platform is designed for people with no programming skills in order to develop context aware mobile apps The reasoning process of the platform has been optimized using the conditions as facts rule pattern The context data gathering process has been optimized using an algorithm that detects the activity of the user
Objectives To develop a rigorous and repeatable method for building effective Bayesian network models for medical decision support from complex unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive redundant and contradictory responses To exploit expert knowledge in the BN development since further data acquisition is usually not possible To ensure the BN model can be used for interventional analysis To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available . Method The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data . While most of the components of the method are based on established work its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life cycle of BN model development . The method is based on two original and recent validated BN models in forensic psychiatry known as DSVM MSS and DSVM P. Results When employed with the same datasets the DSVM MSS demonstrated competitive to superior predictive performance against the state of the art and the DSVM P demonstrated superior predictive performance against the state of the art . More importantly the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence . Conclusions This development process is applicable to any application domain which involves large scale decision analysis based on such complex information rather than based on data with hard facts and in conjunction with the incorporation of expert knowledge for decision support via intervention . The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference rather than based on what data is available and hence forces decision scientists to use available data in a much smarter way . 


Summary: A method for developing Bayesian networks based on both data and knowledge. The method targets complex questionnaire and interviewing medical data. The objective is to provide real world decision support and risk management. The method is based on two original and validated applications in forensic psychiatry. Both applications provide improvements in predictive accuracy and decision support.
Indexing the Web is becoming a laborious task for search engines as the Web exponentially grows in size and distribution . Presently the most effective known approach to overcome this problem is the use of focused crawlers . A focused crawler employs a significant and unique algorithm in order to detect the pages on the Web that relate to its topic of interest . For this purpose we proposed a custom method that uses specific HTML elements of a page to predict the topical focus of all the pages that have an unvisited link within the current page . These recognized on topic pages have to be sorted later based on their relevance to the main topic of the crawler for further actual downloads . In the Treasure Crawler we use a hierarchical structure called T Graph which is an exemplary guide to assign appropriate priority score to each unvisited link . These URLs will later be downloaded based on this priority . This paper embodies the implementation test results and performance evaluation of the Treasure Crawler system . The Treasure Crawler is evaluated in terms of specific information retrieval criteria such as recall and precision both with values close to 50 . Gaining such outcome asserts the significance of the proposed approach . 


Summary: We present the experimental results of a focused Web crawler that combines link based and content based approaches to predict the topical focus of an unvisited page. We present a custom method using Dewey decimal classification system to best classify the subject of an unvisited page into standard human knowledge categories. To prioritize an unvisited URL we use a dynamic flexible and updating hierarchical data structure called T Graph which helps find the shortest path to get to on topic pages on the Web. For the background review the experimental results from several crawlers are presented. We compare our results against other significant focused Web crawlers.
Accurate and reliable prediction of diarrhoea outpatient visits is necessary for the health authorities to ensure the appropriate action for the control of the outbreak . In this study a novel method based on time series decomposition and multi local predictor fusion has been proposed to predict the diarrhoea outpatient visits . For time series decomposition the Ensemble Empirical Mode Decomposition with Adaptive Noise is used to decompose diarrhoea outpatient visits time series into a finite set of Intrinsic Mode Function components and a residue . The IMF components and residue are modeled and predicted respectively by means of Generalized Regression Neural Network as local predictor . Then the prediction results of all components are fusioned using another independent GRNN as fusion predictor to obtain final prediction results . This is the first study on using a EEMDAN and GRNN to constructing an prediction model for diarrhoea outpatient visits prediction problems . The pre procession and post processing techniques are used to take into account the seasonal and trend effects in the datasets for improving the prediction precision of proposed model . The performance of the proposed EEMDAN GRNN model has been compared with Seasonal Auto Regressive Moving Average Single GRNN Wavelet GRNN and also with EEMD GRNN by applying them to predict four real world diarrhoea outpatient visits . The results indicate that the proposed EEMDAN GRNN model provides more accurate prediction results compared to the other traditional techniques . Thus EEMDAN GRNN can be an alternate tool to facilitate the prediction of diarrhoea outpatient visits . 


Summary: A EEMDAN GRNN algorithm is proposed for diarrhoea outpatient visits prediction. Predictor based on decomposition and ensemble principle superior to single predictor. GRNN is a good candidate as predictor for diarrhoea outpatient visits prediction. EEMDAN is superior to EMD and Wavelet for diarrhoea outpatient visits time series.
In the simulation of a chain of manufacturing processes several finite element packages can be employed and for each process or package a different mesh density or element type may be the most suitable . Therefore there is a need for transferring finite element analysis data among packages and mapping it between meshes . This paper presents efficient algorithms for mapping FEA data between meshes with different densities and element types . An in core spatial index is created on the mesh from which FEA data is transferred . The index is represented by a dynamic grid partitioning the underlying space from which nodes and elements are drawn into equal sized cells . Buckets containing references to the nodes indexed are associated with the cells in a many to one correspondence . Such an index makes nearest neighbour searches of nodes and elements much faster than sequential scans . An experimental evaluation of the mapping techniques using the index is conducted . The algorithms have been implemented in the open source finite element data exchange system FEDES . 


Summary: We present efficient algorithms for mapping FEA data between meshes. An in core spatial index is created on the source mesh. Nearest neighbour searches of nodes and elements in the index are very fast. An experimental evaluation of the mapping techniques using the index is conducted. The algorithms have been implemented in the finite element data exchange system FEDES.
A new hybrid mixed stress finite element model for the static and dynamic non linear analysis of concrete structures is presented and discussed in this paper . The main feature of this model is the simultaneous and independent approximation of the stress the strain and the displacement fields in the domain of each element . The displacements along the static boundary which is considered to include inter element boundaries are also directly approximated . To define the approximation bases in the space domain complete sets of orthonormal Legendre polynomials are used . The adoption of these functions enables the use of analytical closed form solutions for the computation of all linear structural operators and leads to the development of very effective p refinement procedures . To represent the material quasi brittle behaviour a physically non linear model is considered by using damage mechanics . A simple isotropic damage model is adopted and to control strain localisation problems a non local integral formulation is considered . To solve the dynamic non linear governing system a time integration procedure based on the use of the HHT method is used . For each time step the solution of the non linear governing system is achieved using an iterative algorithm based on a secant method . The model being discussed is applied to the solution of two dimensional structures . To validate the model to illustrate its potential and to assess its accuracy and numerical efficiency several numerical examples are discussed and comparisons are made with solutions provided by experimental tests and with other numerical results obtained using conventional finite elements . 


Summary: A new hybrid mixed stress finite element model is presented. Independent approximation of stress strain and displacement fields. Static and dynamic physically non linear analysis with isotropic damage models. Use of effective p refinement procedures. Legendre polynomials are used to define the approximation bases.
This paper proposes a new variant of particle swarm optimization namely multiple learning PSO with space transformation perturbation to improve the performance of PSO . The proposed MLPSO STP uses a novel learning strategy and STP . The novel learning strategy allows each particle to learn from the average information on the personal historical best position of all particles and from the information on multiple best positions that are randomly chosen from the top 100p of pbest . This learning strategy enables the preservation of swarm diversity to prevent premature convergence . Meanwhile STP increases the chance to find optimal solutions . The performance of MLPSO STP is comprehensively evaluated in 21 unimodal and multimodal benchmark functions with or without rotation . Compared with eight popular PSO variants and seven state of the art metaheuristic search algorithms MLPSO STP performs more competitively on the majority of the benchmark functions . Finally MLPSO STP shows satisfactory performance in optimizing the operating conditions of an ethylene cracking furnace to improve the yields of ethylene and propylene . 


Summary: A new variant of PSO abbreviated as MLPSO STP is proposed. A novel learning strategy is used to enhance the global search ability. Space transformation perturbation is used to obtain better solutions. MLPSO STP outperforms its peers in terms of searching accuracy and reliability. MLPSO STP is used to optimize the operating conditions of ethylene cracking furnace.
Evidence theory employs a much more general and flexible framework to quantify the epistemic uncertainty and thereby it is adopted to conduct reliability analysis for engineering structures recently . However the large computational cost caused by its discrete property significantly influences the practicability of evidence theory . This paper proposes an efficient response surface method to evaluate the reliability for structures using evidence theory and hence improves its applicability in engineering problems . A new design of experiments technique is developed whose key issue is the search of the important control points . These points are the intersections of the limit state surface and the uncertainty domain thus they have a significant contribution to the accuracy of the subsequent established RS . Based on them a high precise radial basis functions RS to the actual limit state surface is established . With the RS the reliability interval can be efficiently computed for the structure . Four numerical examples are investigated to demonstrate the effectiveness of the proposed method . 


Summary: A RS approach for structural reliability analysis using evidence theory is proposed. A design of experiments technique is proposed to guarantee the RS precision. The results of the numerical examples show pretty good efficiency and precision.
The induction of decision tree searches for relevant characteristics in the data which would allow it to precisely model a certain concept but it also worries about the comprehensibility of the generated model helping human specialists to discover new knowledge something very important in the medical and biological areas . On the other hand such inducers present some instability . The main problem handled here refers to the behavior of those inducers when it comes to high dimensional data more specifically to gene expression data irrelevant attributes may harm the learning process and many models with similar performance may be generated . In order to treat those problems we have explored and revised windowing pruning of the trees generated during intermediary steps of the algorithm the use of the estimated error instead of the training error the use of the error weighted according to the size of the current window and the use of the classification confidence as the window update criterion . The results show that the proposed algorithm outperform the classical one especially considering measures of complexity and comprehensibility of the induced models . 


Summary: We propose several improvements for the windowing algorithm. We evaluated model performance interpretability and stability. Our methodology focuses on the interpretability of the model. Our approach shows differences in terms of interpretability without harming performance. Our approach may yield better classification models.