Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes

Autor: Siyi Tang, Orod Razeghi, Ridhima Kapoor, Mahmood I. Alhusseini, Muhammad Fazal, Albert J. Rogers, Miguel Rodrigo Bort, Paul Clopton, Paul J. Wang, Daniel L. Rubin, Sanjiv M. Narayan, Tina Baykaner

Publikováno v: Tang, Siyi Razeghi, Orod Kapoor, Ridhima Alhusseini, Mahmood I. Fazal, Muhammad Rogers, A. J. Rodrigo Bort, Miguel Clopton, Paul Wang Jiyou, Paul Rubin, Daniel L. 2022 Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes Circulation-Arrhythmia And Electrophysiology 15 8 500 509

Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning metho

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5d1ef2ff551f28af8b7571db296fa95
https://doi.org/10.1161/circep.122.010850