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《Big Data Research》 2018-03
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Chronic disease complications clustering based on ICD-10 diagnoses code

WANG Xiaoxia;JIANG Fusong;WANG Yu;XIONG Yun;School of Computer Science,Fudan University;Shanghai Key Laboratory of Data Science;College of Computer Science and Engineering,Northwest Normal University;Shanghai Sixth People's Hospital;Shanghai Putuo District Center for Disease Control and Prevention;  
Study on the relationship between the chronic disease and the corresponding complications has great theoretical significance and applicable value for patients and clinical medicine. In order to utilize healthcare electronic record more reasonably, preprocessing was needed according to prior medical knowledge for chronic disease complication. The challenge of this work is that medical knowledge should be exploited to label the corresponding complications. To meet these challenges and assist physicians in labeling complications of a target chronic disease, a semi-supervised chronic disease complications clustering algorithm based on ICD-10 code for diagnoses was proposed. Experiments on a real dataset of diabetes electronic healthcare record show that the algorithms are practical and effective.
【Fund】: 国家高技术研究发展计划(“863”计划)基金资助项目(N o.2015AA020105);; 上海市科技发展基金资助项目(No.16JC1400801 No.17511105502)~~
【CateGory Index】: TP311.13
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