Abstract
Unsupervised learning methods can find hidden patterns in data and uncover disease subphenotypes. We investigated unsupervised clustering of echocardiographic variables for grading diastolic dysfunction (DD) and test its concordance and prognostic performance in comparison with expert-guided
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