期刊文献+

基于AI的慢病高危管理系统研究与设计 被引量:7

Research and Design of Risk Management System for Chronic Diseases Based on AI
下载PDF
导出
摘要 目的:研究设计一种基于AI的慢病高危管理及管理效果自动评估系统,建设一种基于慢病知识库的高危管理系统,实现系统自动筛选、人工确认的高危自动手动相结合的筛查系统,及早发现高危慢病因素,提前进行跟踪干预。方法:根据慢病患者电子病历与个人信息等其他信息汇总成为个人健康档案,运用人工智能进行健康状况评估,个性化干预管理。结果:实现患病期间保健档案动态更新,提高慢病患者对危险因素的认知,促进健康行为的形成。结论:系统能够有效提高慢病管理效率和效果,降低慢病死亡率,大量节约医保资金。 Objective: Study and design an AI-based automatic risk management and management effect evaluation system for chronic diseases. Construct a high-risk management system based on the knowledge base of chronic diseases, to realize automatic screening and manual confirmation of high-risk automatic manual combined screening system. Early detection of high-risk chronic disease factors, early follow-up intervention. Methods: According to the electronic medical records of patients with chronic diseases, and other information such as personal information, they are summarized into personal health records, and the artificial intelligence is used to evaluate the health status and conduct personalized intervention management. Results: Achieved the dynamic update of the health records during the illness, improved the awareness of risk factors in patients with chronic diseases, and promoted the changes in healthy behaviors. Conclusion: The system can effectively improve the management efficiency and effect of chronic diseases, reduce the mortality of chronic diseases, and save a lot of medical insurance funds.
作者 帅仁俊 陈平 马力 苏逸飞 SHUAI Ren-Jun;CHEN Ping;MA Li(Vice Director of Nanjing Health Information Center,Nanjing 210003,Jiangsu Province,P.R.C)
出处 《中国数字医学》 2019年第1期21-23,共3页 China Digital Medicine
基金 江苏省重大科技示范-基于大数据和人工智能的"南京都市圈"慢性非传染病综合防控云平台科技示范(编号:BE2018607)~~
关键词 慢病管理 管理效果 评估系统 人工智能 chronic disease management management effect evaluation system artificial intelligence
  • 相关文献

参考文献6

二级参考文献79

  • 1丁永福.信息管理系统在秦巴卫生项目执行中的应用[J].中国初级卫生保健,2005,19(5):90-91. 被引量:1
  • 2WHO. World health report 2002[R]. Geneva:WHO,2002.
  • 3United Nations General Assembly. Political declaration of the high-level meeting of the general assembly on the prevention and control of non-communicable disease[Z]. New York, 2011.
  • 4WHO. Updated revised draft global action plan for the pre- vention and control of non communicable diseases 2013-2020 [Z]. Geneva,2013.
  • 5Wyber R, Vaillancourt S, Perry W, et al. Big data in global health:improving health in low-and middle-income countries [J]. Bull World Health Organ,2015,93(3) :203-208.
  • 6Moons K G, Kengne A P, Woodward M, et al. Risk prediction models: development, internal validation, and assessing the incremental value of a new (bio) marker[J]. Heart,2012,98 (11) :683- 690.
  • 7Toll D B, Janssen K J, Vergouwe Y, et al. Validation, upda- ting and impact of clinical prediction rules:a review[J]. J Clin Epidemiol, 2008,61 ( 2 ) : 1085-1094.
  • 8Kim H C. Clinical utility of novel biomarkers in the prediction of coronary heart disease [J].Korean Circ J, 2012,42 : 223 - 228.
  • 9Oh S M, Stefani K M, Kim H C. Development and Application of Chronic Disease Risk Prediction Models[J]. Yonsei Med J, 2014,55 (4) :853-860.
  • 10Aaron S D,Stephenson A L,Cameron D W,et al. A statistical model to predict one year risk of death in patients with cystic fibrosis[J]. J Clin Epidemiol,2015,68(11) :1336-1345.

共引文献102

同被引文献74

引证文献7

二级引证文献29

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部