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

Journals

  1. Zhu J, Wang Y, Duan S, Liu C, Yin W, Yang Y, Zhu T, Wang J. Multivariable data-driven framework of predictive, preventive, and personalized medicine for long-term atrial fibrillation risk in patients with new-onset obstructive sleep apnea. EPMA Journal 2025;16(4):761 View
  2. Potoupni V, Samaras A, Papadopoulos C, Boulmpou A, Moysiadis T, Zormpas G, Tzikas A, Fragakis N, Giannakoulas G, Vassilikos V. Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions. Medicina 2025;61(11):1937 View

Conference Proceedings

  1. Olariu M, Iftene A. 2025 International Conference on INnovations in Intelligent SysTems and Applications (INISTA). Analysis of Current Applications of Artificial Intelligence in Cardiology Diagnosis with Focus on Explainable AI, Holter Monitoring and Blood Biomarkers View