Treatment effect prediction with adversarial deep learning using electronic health records.

Journal: BMC medical informatics and decision making
PMID: 33317502

Abstract

BACKGROUND: Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption of electronic health records (EHRs) has provided a comprehensive data source for intelligent clinical applications including the TEP investigated in this study.

Authors

  • Jiebin Chu
    College of Biomedical Engineering and Instrument Science, Zhejiang University, PR China.
  • Wei Dong
    Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
  • Jinliang Wang
    CardioCloud Medical Technology (Beijing) Co. Ltd, Beijing, 100084, China.
  • Kunlun He
    Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China.
  • Zhengxing Huang
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.

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