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

Machine Learning (1952) Decision Support for Health Professionals (1460) Cardiac Risk and Cardiac Risk Calculators (91) Clinical Information and Decision Making (1802)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33395, first published .
Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study

Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study

Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study

Authors of this article:

Jinwan Wang1 Author Orcid Image ;   Shuai Wang2 Author Orcid Image ;   Mark Xuefang Zhu1 Author Orcid Image ;   Tao Yang3 Author Orcid Image ;   Qingfeng Yin4 Author Orcid Image ;   Ya Hou4 Author Orcid Image
Article Authors Cited by (11) Tweetations (2) Metrics

Journals

  1. Dehdar Karsidani S, Farhadian M, Mahjub H, Mozayanimonfared A. Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model. BMC Cardiovascular Disorders 2022;22(1) View
  2. Hassanzadeh R, Farhadian M, Rafieemehr H. Hospital mortality prediction in traumatic injuries patients: comparing different SMOTE-based machine learning algorithms. BMC Medical Research Methodology 2023;23(1) View
  3. Wang Z, Guo Z. Intelligent Chinese Medicine: A New Direction Approach for Integrative Medicine in Diagnosis and Treatment of Cardiovascular Diseases. Chinese Journal of Integrative Medicine 2023;29(7):634 View
  4. Kirdeev A, Burkin K, Vorobev A, Zbirovskaya E, Lifshits G, Nikolaev K, Zelenskaya E, Donnikov M, Kovalenko L, Urvantseva I, Poptsova M. Machine learning models for predicting risks of MACEs for myocardial infarction patients with different VEGFR2 genotypes. Frontiers in Medicine 2024;11 View
  5. Najdaghi S, Davani D, Shafie D, Alizadehasl A. Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis. International Urology and Nephrology 2024;57(3):855 View
  6. Lin C, Liu Z, Chu P, Chen J, Wu H, Wen M, Kuo C, Chang T. A multitask deep learning model utilizing electrocardiograms for major cardiovascular adverse events prediction. npj Digital Medicine 2025;8(1) View
  7. Ghasemi P, Greenberg M, Southern D, Li B, White J, Lee J. Personalized decision making for coronary artery disease treatment using offline reinforcement learning. npj Digital Medicine 2025;8(1) View
  8. Miao S, Ji P, Zhu Y, Meng H, Jing M, Sheng R, Zhang X, Ding H, Guo J, Gao W, Yang G, Liu Y. The Construction and Application of a Clinical Decision Support System for Cardiovascular Diseases: Multimodal Data-Driven Development and Validation Study. JMIR Medical Informatics 2025;13:e63186 View
  9. Thakur A, Agasthi P, Chao C, Farina J, Holmes D, Fortuin D, Ayoub C, Arsanjani R, Banerjee I. Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients. Computers in Biology and Medicine 2025;189:109966 View
  10. Li J, Yang H, Zhang Y, Yan J, Tian J, Zhang Y. Developing a multi-label learning model to predict major adverse cardiovascular events in patients with unstable angina pectoris: A prospective cohort study. International Journal of Medical Informatics 2025;203:106037 View

Conference Proceedings

  1. Queyam A, Dixit A, Kumar I, Gupta S. 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT). Simulation and Analysis of Heart Disease Predictor Using Machine Learning Techniques View