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New Methods (1716) Clinical Information and Decision Making (3455) Cardiology (274) Decision Support for Health Professionals (2074) Machine Learning (2992) Cardiac Disease Management (180) Cardiac Risk and Cardiac Risk Calculators (157)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78402, first published .
Doctor interacting with a holographic heart and medical data on a futuristic display.

Multimodal Data–Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, Cross-Regional Cohort Study

Multimodal Data–Driven Explainable Prognostic Model for Major Adverse Cardiovascular Events Prediction in Patients With Unstable Angina and Heart Failure With Preserved Ejection Fraction: Multicenter, Cross-Regional Cohort Study

Authors of this article:

Yijun Wang1 Author Orcid Image ;   Yaoling Wang1 Author Orcid Image ;   Yujie Luan1 Author Orcid Image ;   Menglei Hao1 Author Orcid Image ;   Wenyuan Yin2 Author Orcid Image ;   Bing Wu3 Author Orcid Image ;   Tongjian Zhu4 Author Orcid Image ;   Jiajun Zhu5 Author Orcid Image ;   Bowen Zhou6 Author Orcid Image ;   Long Tang7 Author Orcid Image ;   Jun Wang8 Author Orcid Image ;   Jinhui Wu1 Author Orcid Image
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  2. Wang J, Duan S, Li H, Wu S, Zhao P, Zhu T, Tang S, Zhu J, Tang B, Liu J. Development and Validation of a Prognostic Nomogram for Post–Transcatheter Aortic Valve Replacement Heart Failure Hospitalization in Patients With Concurrent Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction: A Multicenter Study. Journal of the American Heart Association 2026;15(6) View
  3. Lin Y, Qin Y, Ou K, Zhu J, Liao B. Multicenter development and validation of machine-learning risk models to predict procedural complete revascularization and in-hospital heart failure in STEMI patients treated with primary PCI. Frontiers in Cardiovascular Medicine 2026;13 View
  4. Zhang Y, Zhang X, Song H, Hu Q, Nan J. Predicting short-term composite outcome risk in heart failure patients using a machine learning model incorporating UHR: a retrospective cohort study. Frontiers in Nutrition 2026;13 View
  5. Xiao P, Cheng H, Hou D, Yan Y, Dong H, Liu J, Liu L, Li Y, Lan L, Xiong J, Mi J. Phenotypic Heterogeneity of Obesity and Short‐Term Cardiometabolic Risk Factors Transitions: A Population‐Based Cohort Study. Diabetes, Obesity and Metabolism 2026 View