Prediction of chronic disease occurrence using EHR data: A pilot study in Hong Kong
By Dr. Qingpeng ZHANG
Department of Systems Engineering and Engineering Management
City University of Hong Kong
Chronic diseases are major causes of morbidity and mortality worldwide. Many chronic diseases are preventable through effective disease management and prevention programs. In addition, some relations between diseases are suspected to be hidden. Doctors need a good decision support system for reliable risk assessment and early detection of chronic diseases. In this ongoing research, we proposed a tensor factorization based model to predict the near-future occurrences of popular chronic diseases at individual patient level using the admission record of the internal medicine department of a major public hospital in Hong Kong. Experiments demonstrated the feasibility and good performance of the proposed approach.
Qingpeng Zhang is an Assistant Professor in the Department of Systems Engineering and Engineering Management at City University of Hong Kong. He received the B.S. degree in Automation from Huazhong University of Science and Technology, and the M.S. degree in Industrial Engineering and the Ph.D. degree in Systems and Industrial Engineering with a minor in Management Information Systems from The University of Arizona. Prior to joining CityU, he worked as a Postdoctoral Research Associate with The Tetherless World Constellation (Department of Computer Science) at Rensselaer Polytechnic Institute. He also worked as PhD intern at the Pacific Northwest National Laboratory and Chinese Academy of Sciences. His research interests include social computing, healthcare informatics and data analytics, complex networks, and semantic web.
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