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Integration of decision-support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

Journal of systems Engineering and Electronics 1996年第2期7卷 35-54页

作者： David McSherry(School of Information and Software Engineering,Faculty of Informatics, University of Ulster,Coleraine BT52 1SA, Northern Ireland) School of Information and Software Engineering Faculty of Informatics University of Ulster Coleraine BT52 ISA Northern Ireland

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

关键词： Knowledge-based systems decision-support systems Knowledge acquisition Multiple criteria decision making Operational research Analytic hierarchy process

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Heart Rate Variability Applied to Short-Term Cardiovascular Event Risk Assessment

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Engineering（科研） 2013年第10期5卷 237-243页

作者： Simao Paredes Teresa Rocha Paulo de Carvalho Jorge Henriques Ramona Cabiddu Joao Morais Computer Science and systems Engineering Department Polytechnic Institute of Coimbra(IPC/ISEC)CoimbraPortugal Centre for Informatics and systems of the University of Coimbra Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal Dipartimento di Elettronica Informazione e BioingegneriaPolitecnico di MilanoMilanoItaly CardiologyDepartment Leiria-Pombal Hospital CentreLeiriaPortugal

Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Therefore, there are several well known risk assessment tools, unfortunately they present some *** work addresses this problem with two different methodologies:1) combination of risk assessment tools based on fusion of Bayesian classifiers complemented with genetic algorithm optimization;2) personalization of risk assessment through the creation of groups of patients that maximize the performance of each risk assessment tool. This last approach is implemented based on subtractive clustering applied to a reduced-dimension *** methodologies were developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva-tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies:1) Santa Cruz Hospital, Portugal, N=460 patients;2)LeiriaPombal Hospital Centre, Portugal, N=99 *** work improved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%, 81.5%. Besides this enhancement, the proposed methodologies allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification) should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters derived from heart rate variability (HRV), is introduced in this work. HRV is a strong and independent predictor of mortality in patien

关键词： CVD Risk Assessment Knowledge Management Management of Cardiovascular Diseases decision-support systems

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