Evolutionary rule decision using similarity based associative chronic disease patients

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• 作者：Hoill Jung (1)
  JungGi Yang (2)
  Ji-In Woo (3)
  Byung-Mun Lee (4)
  Jinsong Ouyang (5)
  Kyungyong Chung (6)
  YoungHo Lee (7)
  
  1. Intelligent System Lab. ; School of Computer Information Engineering ; Sangji University ; 83 ; Sangjidae-gil ; Wonju-si ; Gangwon-do聽 ; 220-702 ; Korea
  2. School of Computer Science ; Gachon University ; 1342 ; Seongnamdaero ; Sujeong-gu ; Seongnam-si ; Gyeonggi-do ; Korea
  3. Department of Computer Science ; Gachon University ; 191 ; Hambakmoero ; Yeonsu-gu ; Incheon聽 ; 406-799 ; Korea
  4. Department of Computer Science ; Gachon University ; 1342 ; Seongnamdaero ; Sujeong-gu ; Seongnam-si ; Gyeonggi-do ; Korea
  5. Department of Computer Science ; California State University Sacramento ; 6000 J Street ; Sacramento ; CA ; USA
  6. School of Computer Information Engineering ; Sangji University ; 83 ; Sangjidae-gil ; Wonju-si ; Gangwon-do ; 聽220-702 ; Korea
  7. Department of Computer Science ; Gachon University ; 1342 ; Seongnamdaero ; Sujeong-gu ; Seongnam-si ; Gyeonggi-do ; Korea
  
• 关键词：Data Mining ; Clinical decision support system ; Chronic disease patients ; Telemedicine ; U ; Healthcare ; IT convergence
• 刊名：Cluster Computing
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：18
• 期：1
• 页码：279-291
• 全文大小：2,667 KB
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• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Operating Systems
  Computer Communication Networks
  
• 出版者：Springer Netherlands
• ISSN：1573-7543

文摘

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.