An Explainable Machine Learning Model Integrating Clinical Parameters for Identifying Left Atrial Appendage Thrombus in Non-valvular Atrial Fibrillation.
OBJECTIVE This study aimed to develop a predictive model for left atrial appendage thrombus (LAAT) in patients with non-valvular atrial fibrillation by integrating clinical and echocardiographic features using machine learning algorithms. The SHapley Additive exPlanations (SHAP) framework was applied to interpret the m...