Importance-aware personalized learning for early risk prediction using static and dynamic health data.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Mar 18, 2021

Abstract

OBJECTIVE: Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data.

Authors

  • Qingxiong Tan
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong.
  • Mang Ye
  • Andy Jinhua Ma
    Hong Kong Baptist University, Hong Kong.
  • Terry Cheuk-Fung Yip
    Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Grace Lai-Hung Wong
    Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; State Key Laboratory of Digestive Disease, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China.
  • Pong C Yuen

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