https://www.selleckchem.com/
OBJECTIVES The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac events (MACE) in an individual patient. BACKGROUND Patient similarity analysis is an evolving paradigm for precision medicine in which patients are clustered or classified based on their similarities in several clinical features. METHODS A retrospective cohort of 866 patients was used to develop a network architecture using 9 e
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