Abstract
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.
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Acknowledgement
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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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, S., Liu, L., Li, H., Cui, L. (2017). Collaborative Prediction Model of Disease Risk by Mining Electronic Health Records. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_7
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DOI: https://doi.org/10.1007/978-3-319-59288-6_7
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