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We investigate the molecular mechanism of inherited arrhythmias using a multiscale model. The model has numerous parameters some of which are estimated with voltage clamp data. Errors in the estimation of parameters have a dramatic effect on the investigation of the mechanism of arrhythmias. In some cases, these errors may prevent seeing the creation of a premature cardiac beat. We present an analysis...
Surrogate data method is commonly required to confirm the non-accidental nature of simultaneous fluctuations of heart rate (HR) and blood pressure (BP) due to the spontaneous baroreceptor reflex (sBRR) mechanism. Previously proposed finite, ergodic Markov model with memory, enables derivation of all BRR temporal parameters for isodistributional (ID) surrogate data in closed form, thus eliminating...
Primary hyperhidrosis, a disorder characterized by an excessive sweating, has been treated by endoscopic thoracic sympathectomy. As a consequence of the surgery, patients improved their overall quality of life. Their day-by-day activities are not affected, or are less affected, by this disorder, and their emotional state verifies a significant improvement, from a situation of shame and self-punishing...
The primary objective of disparities research is to model the differences across multiple groups and identify the groups that behave significantly different from each other. Independently generating various decision trees for different subsets of the data will not allow us to study the impact of the various attributes on these different subgroups. We propose a novel technique for inducing similar...
Every organization needs to create clearly formatted reports using some reporting software. Created reports can be used within organization either as a base for further analysis and researches, or as set of data formatted as a document that can be delivered to employees, customers, and partners. Since reporting software usually is not used just by IT professionals, it ought to have simple and easily...
In this paper, we present a novel technique of building hybrid decision support systems which integrates traditional decision support systems with agent based models for use in breast cancer analysis for better prediction and recommendation. Our system is based on using queries from data (converted to a standardized electronic template) to provide for simulation variables in an agent-based model....
Using a neonatal intensive care unit (NICU) case study, this work investigates the current cross industry standard process for Data Mining (CRISP-DM) approach for modeling intelligent data analysis (IDA)-based systems that perform temporal data mining (TDM). The case study highlights the need for an extended CRISP-DM approach when modeling clinical systems applying data mining (DM) and temporal abstraction...
Cardiac risk factor assessment requires a classification system that is robust to the interaction and uncertainty of input factors, as well as being interpretable on the decision made. To meet the requirements, we made use of neuro-fuzzy methods, a certain novelty in cardiac risk assessment.Statistic data of 165 patients including sex, age, LDL, blood pressure, and Myocard-brain Creatinine Phosphokinase...
In an association study, empirical evidences support the commonality of gene-gene interactions. Although genetic factors play an important role in many human diseases, multiple genes or genes and environmental factors may ultimately influence individual risk for these disease. However, such interactions are difficult to detect. In this paper, we propose a penalized area under ROC curve (AUC) maximization...
Missing values are common in medical datasets and may be amenable to data imputation when modelling a given data set or validating on an external cohort. This paper discusses model averaging over samples of the imputed distribution and extends this approach to generic non-linear modelling with the Partial Logistic Artificial Neural Network (PLANN) regularised within the evidence-based framework with...
A constantly increasing number of applications from various scientific sectors are finding their way towards adopting grid technologies in order to take advantage of their capabilities: the advent of grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. In this paper we present a grid-based approach for aggregation of data that are obtained...
Graphical models allow scientific prior knowledge to be incorporated into the statistical analysis of data, whilst also providing a vivid way to represent and communicate this knowledge. In this paper we develop a graphical model of the immune system as a means of analyzing immunological data from the Manchester asthma and allergy study (MAAS). The analysis is achieved using the Infer.NET tool which...
Classifiers based on parametric or non-parametric learning methods have different advantages and disadvantages. To take advantage of the strengths of both methods, we propose an algorithm that combines a parametric model (logistic regression) with a non-parametric classification method (k-nearest neighbors). This combination is based on a measure of appropriateness that uses a heuristic to decide...
In the past few years a number of algorithms for cardiovascular risk stratification have been proposed to the medical community but a big question has been remained unsolved: From among alternative sets of cardiac risk factors which ones are more significant in cardiac risk stratification? In this paper a hybrid neuro-fuzzy classifier, IRIDIA Method for Neuro-fuzzy Identification and Data Analysis,...
Current approaches to the analysis of protein structure are time-consuming due to the lack of integration and incompatibility of data among the processing tools utilized. In addition, the enormous amount of experimental data generated is typically stored in flat file formats that cannot easily be managed or queried. Available databases that support the storage of protein structure data use data models...
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