Collaborative Prediction Model of Disease Risk by Mining Electronic Health Records

  • Shuai Zhang
  • Lei Liu
  • Hui Li
  • Lizhen CuiEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


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.


Electronic Health Records Temporal graph Collaborative prediction Disease risk profile 



This work is partially supported by NSFC No. 61303005, 61572295; the Innovation Method Fund of China No. 2015IM010200; SDNSFC No. ZR2014FM031; the Science and Technology Development Plan Project of Shandong Province No. 2014GGX101019, 2015GGX101007, 2015GGX 101015; the Shandong Province Independent Innovation Major Special Project No. 2015ZDJQ010 02, 2015ZDXX0201B03; the Fundamental Research Funds of Shandong University No. 2014JC025, 2015JC031.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina

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