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Erschienen in: Cluster Computing 1/2015

01.03.2015

Evolutionary rule decision using similarity based associative chronic disease patients

verfasst von: Hoill Jung, JungGi Yang, Ji-In Woo, Byung-Mun Lee, Jinsong Ouyang, Kyungyong Chung, YoungHo Lee

Erschienen in: Cluster Computing | Ausgabe 1/2015

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Abstract

Efficient healthcare management has increasingly drawn much attention in healthcare sector along with recent advances in IT convergence technology. Population aging and a shift from an acute to a chronic disease with a long duration of illness have urgently necessitated healthcare service for efficient, systematic health management. Clinical decision support system (CDSS) is an integrated healthcare system that effectively guides health management and promotion, recommendation for regular health check-up, tailor-made diet therapy, health behavior change for self-care, alert service for drug interaction in patients with chronic diseases with a high prevalence. Although CDSS rule-based algorithm aids guidelines and decision making according to a single chronic disease, it is unable to inform unique characteristics of each chronic disease and suggest preventive strategies and guidelines of complex diseases. Therefore, this study proposes evolutionary rule decision making using similarity based associative chronic disease patients to normalize clinical conditions by utilizing information of each patient and recommend guidelines corresponding detailed conditions in CDSS rule-based inference. Decision making guidelines of chronic disease patients could be systematically established according to various environmental conditions using database of patients with different chronic diseases.

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Metadaten
Titel
Evolutionary rule decision using similarity based associative chronic disease patients
verfasst von
Hoill Jung
JungGi Yang
Ji-In Woo
Byung-Mun Lee
Jinsong Ouyang
Kyungyong Chung
YoungHo Lee
Publikationsdatum
01.03.2015
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 1/2015
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-014-0376-x

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