Evaluation of Quantitative Decision‐Making for Rhythm Management of Atrial Fibrillation Using Tabular Q‐Learning

Background Rhythm management is a complex decision for patients with atrial fibrillation (AF). Although clinical trials have identified subsets of patients who might benefit from a given rhythm-management strategy, for individual patients it is not always clear which strategy is expected to have the...

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Veröffentlicht in:	Journal of the American Heart Association Jg. 12; H. 9; S. e028483
Hauptverfasser:	Barrett, Christopher D., Suzuki, Yuto, Hussein, Sundos, Garg, Lohit, Tumolo, Alexis, Sandhu, Amneet, West, John J., Zipse, Matthew, Aleong, Ryan, Varosy, Paul, Tzou, Wendy S., Banaei‐Kashani, Farnoush, Rosenberg, Michael A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England John Wiley and Sons Inc 02.05.2023 Wiley
Schlagworte:	Anti-Arrhythmia Agents - therapeutic use Artificial Intelligence atrial fibrillation Atrial Fibrillation - drug therapy Atrial Fibrillation - therapy Electric Countershock Humans Original Research Q‐learning rate control reinforcement learning Retrospective Studies rhythm control atrial fibrillation Q‐learning rate control artificial intelligence reinforcement learning rhythm control unsupervised learning
ISSN:	2047-9980, 2047-9980
Online-Zugang:	Volltext
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