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

Clinical Information and Decision Making (1915) Machine Learning (2076) Cardiac Surgery (20) Heart Valve Disease Research (6) Cardiac Risk and Cardiac Risk Calculators (102) Cardiology (224)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70587, first published .
Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study

Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study

Multimodal Visualization and Explainable Machine Learning–Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study

Authors of this article:

Jun Wang1, 2, 3 Author Orcid Image ;   Jiajun Zhu4 Author Orcid Image ;   Hui Li1 Author Orcid Image ;   Shili Wu1, 5 Author Orcid Image ;   Siyang Li6 Author Orcid Image ;   Zhuoya Yao1 Author Orcid Image ;   Tongjian Zhu6 Author Orcid Image ;   Bi Tang1, 2, 3 Author Orcid Image ;   Shengxing Tang7 Author Orcid Image ;   Jinjun Liu1, 2, 3 Author Orcid Image
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