标题:Collaborative Prediction Model of Disease Risk by Mining Electronic Health Records
作者:Zhang, Shuai; Liu, Lei; Li, Hui; Cui, Lizhen
通讯作者:Cui, LZ
作者机构:[Zhang, Shuai; Liu, Lei; Li, Hui; Cui, Lizhen] Shandong Univ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China.
会议名称:12th International Conference on Collaborate Computing - Networking, Applications and Worksharing (CollaborateCom)
会议日期:NOV 10-11, 2016
来源:COLLABORATE COMPUTING: NETWORKING, APPLICATIONS AND WORKSHARING, COLLABORATECOM 2016
出版年:2017
卷:201
页码:71-82
DOI:10.1007/978-3-319-59288-6_7
关键词:Electronic Health Records; Temporal graph; Collaborative prediction;; Disease risk profile
摘要:Patient Electronic Health Records (EHR) is one of the major carriers for conducting preventative medicine research. However, the heterogeneous and longitudinal properties make EHRs analysis an inherently challenge. To address this issue, this paper proposes CAPM, a Collaborative Assessment Prediction Model based on patient temporal graph representation, which relies only on a patient EHRs using ICD-10 codes to predict future disease risks. Firstly, we develop a temporal graph for each patient EHRs. Secondly, CAPM uses hybrid collaborative filtering approach to predict each patient's greatest disease risks based on their own medical history and that of similar patients. Moreover, we also calculate the onset risk with the corresponding diseases in order to take action at the earliest signs. Finally, we present experimental results on a real world EHR dataset, demonstrating that CAPM performs well at capturing future disease and its onset risks.
收录类别:CPCI-S;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021698155&doi=10.1007%2f978-3-319-59288-6_7&partnerID=40&md5=4937b6bdef216cdb6cdd992e2427e981
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