Integration of Different Risk Assessment Tools to Improve Stratification of Patients with Coronary Artery Disease.
Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to increase as the European population ages. In this context, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Some useful tools have been developed to predic...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 10; pp. 1069 - 1084 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110401384&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110401384 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2015 vid: 53 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110401384 110401384 NLM26215518 110401384 10.1007/s11517-015-1342-3 NLM26215518 110401384 ppf: 1069 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Integration of Different Risk Assessment Tools to Improve Stratification of Patients with Coronary Artery Disease. aug: au: Paredes, S. Rocha, T. Carvalho, P. Henriques, J. Morais, J. Ferreira, J. de Carvalho, P affil: Computer Science and Systems Engineering Department, Polytechnic Institute of Coimbra (IPC/ISEC), Rua Pedro Nunes 3030-199 Coimbra Portugal sug: subj: Coronary Arteriosclerosis Diagnosis Coronary Arteriosclerosis Epidemiology Coronary Arteriosclerosis Classification Decision Support Systems, Clinical Sensitivity and Specificity Risk Factors Aged Male Algorithms Risk Assessment Methods Female Middle Age Human Aged: 65+ years Middle Aged: 45-64 years Male Female ab: Cardiovascular disease (CVD) causes unaffordable social and health costs that tend to increase as the European population ages. In this context, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Some useful tools have been developed to predict the risk of occurrence of a cardiovascular disease event (e.g. hospitalization or death). However, these tools present some drawbacks. These problems are addressed through two methodologies: (i) combination of risk assessment tools: fusion of naïve Bayes classifiers complemented with a genetic optimization algorithm and (ii) personalization of risk assessment: subtractive clustering applied to a reduced-dimensional space to create groups of patients. Validation was performed based on two ACS-NSTEMI patient data sets. This work improved the performance in relation to current risk assessment tools, achieving maximum values of sensitivity, specificity, and geometric mean of, respectively, 79.8, 83.8, and 80.9 %. Additionally, it assured clinical interpretability, ability to incorporate of new risk factors, higher capability to deal with missing risk factors and avoiding the selection of a standard CVD risk assessment tool to be applied in the clinical practice. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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