aceAn 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 model and quantify the contribution of each predictor, thereby enhancing the accuracy and clinical interpretability of LAAT risk prediction. Methods In this retrospective study, 325 patients with no…
A machine learning model with SHAP explanations uses readily available clinical and echocardiographic variables to predict thrombus in the left atrial appendage, which may support individualized anticoagulation decisions.
The model was developed in a retrospective study of 325 patients at a single institution, so its generalizability and prospective clinical performance are not established in the supplied text.
Evidence
- Peer-reviewedUltrasound in Medicine & Biology2026-10-09
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Truvace Impact Record TRV-2026-1650, v1: “An Explainable Machine Learning Model Integrating Clinical Parameters for Identifying Left Atrial Appendage Thrombus in Non-valvular Atrial Fibrillation.” Truvace, 2026-10-17. /record/TRV-2026-1650 (accessed at citation time). sha256 82b635599ec482a6…
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