Can machine learning-based unsupervised cluster analysis identify distinct phenogroups with differing clinical characteristics and long-term outcomes in patients with HFpEF?
Patients with Heart Failure with Preserved Ejection Fraction (HFpEF)
Machine learning-based unsupervised cluster analysis (phenomapping)
Identification of phenogroups with distinct clinical characteristics and long-term outcomes
Machine learning-based unsupervised cluster analysis can successfully identify distinct phenogroups of HFpEF patients with differing clinical characteristics and long-term outcomes.
Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.
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Matthew W. Segar
Electrophysiology
Kershaw V. Patel
Preventive Cardiology
Colby Ayers
Preventive Cardiology
European Journal of Heart Failure
The University of Texas Southwestern Medical Center
Cleveland Clinic
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Segar et al. (Mon,) studied this question.
synapsesocial.com/papers/69d575396e4506aa303c145f — DOI: https://doi.org/10.1002/ejhf.1621
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