Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome.

Journal: BMC medical informatics and decision making
PMID: 30626381

Abstract

BACKGROUND: Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effects of ACS treatments. Most existing tools are specific to predict MACE by mainly using static patient features and neglecting dynamic treatment information during learning.

Authors

  • Huilong Duan
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.
  • Zhoujian Sun
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Key Lab for Biomedical Engineering of Ministry of Education, Zheda Road, Hangzhou, China.
  • Wei Dong
    Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
  • Zhengxing Huang
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.

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