Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Journal: Journal of medical Internet research
PMID: 38728685

Abstract

BACKGROUND: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability.

Authors

  • Puguang Xie
    Chongqing Emergency Medical Centre, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.
  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Jun Xiao
    Zhejiang University, 38 Zheda Road, Hangzhou 310058, Zhejiang, China.
  • Fan Xu
    Department of Public Health, Chengdu Medical College, Sichuan, China.
  • Jingyang Liu
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zihang Chen
    Bioengineering College, Chongqing University, Chongqing, China.
  • Weijie Zhao
    Bioengineering College, Chongqing University, Chongqing, China.
  • Siyu Hou
    MOE Key Laboratory of Bioinformatics, TCM-X Centre/Bioinformatics Division, BNRIST, Tsinghua University, Beijing 10084, China.
  • Dongdong Wu
    Department of Information, Research Institute of Field Surgery, Daping Hospital of Army Medical University, 10 Changjiang Access Road, Chongqing, 400042, China.
  • Yu Ma
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Jingjing Xiao
    Department of Hepatobiliary Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, P. R. China.

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