Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis.

Journal: International journal of medical informatics
PMID: 40073650

Abstract

BACKGROUND: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial.

Authors

  • Yuan Zhang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Huan Liu
    Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
  • Qingxia Huang
    Research Center of Traditional Chinese Medicine, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin 130117, China.
  • Wantong Qu
    Department of Cardiology, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun 130000 Jilin, China.
  • Yanyu Shi
    College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin 130000, China.
  • Tianyang Zhang
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Jinjin Chen
    School of Economics and Management, Yanshan University, Qinhuangdao, 066004, China.
  • YuQing Shi
    Jinjiang Third Hospital, Quanzhou 362000, China.
  • Ruixue Deng
    College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin 130000, China.
  • Ying Chen
    Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Zepeng Zhang
    Research Center of Traditional Chinese Medicine, The First Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin 130117, China. Electronic address: zzp9762@126.com.

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