Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction.

Journal: Clinical medicine (London, England)
Published Date:

Abstract

OBJECTIVE: We aim to develop and validate an interpretable machine-learning model that can provide critical information for the clinical treatment of critically ill patients with acute myocardial infarction (AMI).

Authors

  • Rui Yang
    Department of Biomedical Informatics, Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore.
  • Tao Huang
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Renqi Yao
    Translational Medicine Research Center, Fourth Medical Center and Medical Innovation Research Division of the Chinese PLA General Hospital, Beijing, China.
  • Di Wang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Yang Hu
    Kweichow Moutai Co., Ltd, Renhuai, Guizhou 564501, China.
  • Longbing Ren
    China Center for Health Development Studies, Peking University, Beijing, 100191, China.
  • Shaojie Li
    Department of Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China.
  • Yali Zhao
    School of Electronics and Information, Xi'an Polytechnic University, Xi'an, 710000, China.
  • Zhijun Dai
    State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China; Laboratory for Marine Geology, Qingdao Marine Science and Technology Center, Qingdao 266061, China. Electronic address: zjdai@sklec.ecnu.edu.cn.