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

Development and Evaluation of Research Methods, Instruments and Tools (511) Clinical Information and Decision Making (1434) Cardiology (164) Decision Support for Health Professionals (1210) Machine Learning (1501) Coronary Heart Disease (81) Cardiac Disease Management (100)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48487, first published .
Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study

Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study

Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study

Authors of this article:

Pin Zhang1, 2 Author Orcid Image ;   Lei Wu1 Author Orcid Image ;   Ting-Ting Zou1 Author Orcid Image ;   ZiXuan Zou1 Author Orcid Image ;   JiaXin Tu1 Author Orcid Image ;   Ren Gong3 Author Orcid Image ;   Jie Kuang1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Zhang S, Yang G, Chen Y, Liu W. miR-223-5p serves as a diagnostic biomarker for acute coronary syndrome and its predictive value for the clinical outcome after PCI. BMC Cardiovascular Disorders 2024;24(1) View
  2. Li P, Zhang W, Wu B. Adherence to Cardiac Rehabilitation in Patients with Acute Myocardial Infarction After PCI: A Scoping Review. Journal of Multidisciplinary Healthcare 2024;Volume 17:4165 View
  3. Yan R, Jiang N, Zhang K, He L, Tuerdi S, Yang J, Ding J, Li Y. Risk prediction of arrhythmia after percutaneous coronary intervention in patients with acute coronary syndrome: A systematic review and meta-analysis. International Journal of Medical Informatics 2025;195:105711 View