Using Machine Learning to Predict MACEs Risk in Patients with Premature Myocardial Infarction.

Journal: Reviews in cardiovascular medicine
Published Date: May 20, 2025

Abstract

BACKGROUND: The study aimed to develop an interpretable machine learning (ML) model to assess and stratify the risk of long-term major adverse cardiovascular events (MACEs) in patients with premature myocardial infarction (PMI) and to analyze the key variables affecting prognosis.

Authors

  • Jing-Xian Wang
    Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
  • Miao-Miao Liang
    Clinical School of Thoracic, Tianjin Medical University, 300070 Tianjin, China.
  • Peng-Ju Lu
    Department of Cardiology, Tianjin Chest Hospital, 300222 Tianjin, China.
  • Zhuang Cui
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Yan Liang
    Department of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States.
  • Yu-Hang Wang
    Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
  • An-Ran Jing
    Clinical School of Thoracic, Tianjin Medical University, 300070 Tianjin, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Meng-Long Zhang
    Clinical School of Thoracic, Tianjin Medical University, 300070 Tianjin, China.
  • Yin Liu
    School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
  • Chang-Ping Li
    Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin 300070, China.
  • Jing Gao
    Department of Gastroenterology 3, Hubei University of Medicine, Renmin Hospital, Shiyan, Hubei, China.

Keywords

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