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Customers with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage based on fundamental aetiology, disease progression, and comorbidities. This research is designed to capture their cardiopulmonary complexity by utilizing a machine-learning (ML)-based phenotyping strategy. Data had been obtained from 1426 clients undergoing mitral device transcatheter edge-to-edge fix (MV TEER) for MR. The ML design was developed using 609 clients (

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