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

Development and Evaluation of Research Methods, Instruments and Tools (744) Clinical Information and Decision Making (1915) Cardiology (224) Decision Support for Health Professionals (1539) Machine Learning (2076) Coronary Heart Disease (109) Cardiac Disease Management (156)

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 (9) 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
  4. Xu L, Lin J, Xia J, Chen D, He G. The diagnostic and prognostic value of serum miR-199a-5p combined with echocardiography in acute myocardial infarction. Journal of Cardiothoracic Surgery 2025;20(1) View
  5. Yang L, Du L, Ge Y, Ou M, Huang W, Wang X. Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes. BMC Cardiovascular Disorders 2025;25(1) View
  6. Li W, Yan D, Hu W, Su X, Zhang Z. Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification. Frontiers in Artificial Intelligence 2025;8 View
  7. Jolfayi A, Nasrollahizadeh A, Nasrollahizadeh A, Pishraft-Sabet H, Azimi A, Jenab Y, Mehrani M, Hosseini K, Soleimani H. Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention. BMC Medical Informatics and Decision Making 2025;25(1) View

Conference Proceedings

  1. R M, A S, B P. 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC). Enhanced Hypertrophic Cardiomyopathy Detection Using Machine Learning on ECG Images View
  2. Wahid M, Tafreshi R, Al-Hijji M, Khalil C. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). A Machine Learning Approach to Predictive Modeling of Cardiovascular Events View