Tabular Q-learning using batch gradient descent provided a dynamic approach for atrial fibrillation rhythm management, changing the optimal strategy in some clusters from cardioversion to ablation.
Does Tabular Q-learning provide a dynamic and interpretable approach to clinical decision-making for rhythm management in atrial fibrillation?
Tabular Q-learning provides a dynamic and interpretable artificial intelligence approach for clinical decision-making in atrial fibrillation rhythm management.
). We then demonstrated application of dynamic learning by updating the Q-table prospectively using batch gradient descent, in which the optimal strategy in some clusters changed from cardioversion to ablation. Conclusions Tabular Q-learning provides a dynamic and interpretable approach to apply artificial intelligence to clinical decision-making for atrial fibrillation. Further work is needed to examine application of Q-learning prospectively in clinical patients.
Barrett et al. (Sat,) conducted a other in Atrial fibrillation. Tabular Q-learning was evaluated. Tabular Q-learning using batch gradient descent provided a dynamic approach for atrial fibrillation rhythm management, changing the optimal strategy in some clusters from cardioversion to ablation.