This paper is in the following e-collection/theme issue:

Artificial Intelligence (2261) Clinical Information and Decision Making (2014) Machine Learning (2182) Decision Support for Health Professionals (1599) Cardiac Risk and Cardiac Risk Calculators (113) Myocardial Infarction (Heart Attack) (56)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50067, first published .
A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry

A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry

A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry

Authors of this article:

Jingang Yang1 Author Orcid Image ;   Yingxue Li2 Author Orcid Image ;   Xiang Li2 Author Orcid Image ;   Shuiying Tao1 Author Orcid Image ;   Yuan Zhang2 Author Orcid Image ;   Tiange Chen2 Author Orcid Image ;   Guotong Xie2 Author Orcid Image ;   Haiyan Xu1 Author Orcid Image ;   Xiaojin Gao1 Author Orcid Image ;   Yuejin Yang1 Author Orcid Image
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Journals

  1. Tang N, Liu S, Li K, Zhou Q, Dai Y, Sun H, Zhang Q, Hao J, Qi C. Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods. Frontiers in Cardiovascular Medicine 2024;11 View
  2. Yang Y, Tang J, Ma L, Wu F, Guan X. A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models. BMC Medical Informatics and Decision Making 2025;25(1) View
  3. Li W, Lei P, Dong R, He S, Zhang Z, Han B. Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance. Reviews in Cardiovascular Medicine 2025;26(9) View
  4. Jian R, Zhang J, Zeng Y, Zhou T, Wu Y, Wu L, Yu Y, Xi C. In-hospital mortality risk prediction models for patients with acute coronary syndrome: a systematic review and meta-analysis. Frontiers in Cardiovascular Medicine 2025;12 View
  5. Ge T, Hu J, Zhou Y. Innovations in the Treatment of Acute Myocardial Infarction in the Era of Precision and Intelligence: Transitioning From Reperfusion Strategies to Regenerative Medicine. The Heart Surgery Forum 2025;28(11) View
  6. Kasim S, Malek S, Cheen S, Fatin P, Ning K, Hamidi H, Ahmad W, Ibrahim K, Negishi K, Sulaiman M, Fong A. Machine learning-based prediction of mortality risk from air pollution-induced acute coronary syndrome in the Western Pacific region. Scientific Reports 2026;16(1) View
  7. Yu B, Cho J, Kim H, Hwang S, Hwang J, Kim S, Oh J, Lee S, Kim D, Seok J, Kim K, Lee J, Yon D, Kang W. On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality. BJS Open 2025;10(1) View
  8. Xie W, Shi R, Xiang J, Chen B, An D, Zhou Y, Zhao L, Pu J, Wu L. Machine Learning Using Clinical and Cardiac MRI Features to Predict Long-term Outcomes in Acute STEMI. Radiology 2026;318(2) View

Books/Policy Documents

  1. Sanyal P, Kuthe M, Maurya S, Partakke S, Ismail F, Garg R. Smart Trends in Computing and Communications. View