Phenomapping of Patients with Heart Failure with Preserved Ejection Fraction Using Machine Learning-Based Unsupervised Cluster Analysis

Structured PICO

Can machine learning-based unsupervised cluster analysis identify distinct phenogroups with differing clinical characteristics and long-term outcomes in patients with HFpEF?

Population

Patients with Heart Failure with Preserved Ejection Fraction (HFpEF)

Intervention

Machine learning-based unsupervised cluster analysis (phenomapping)

Outcome

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.

Abstract

Machine learning-based cluster analysis can identify phenogroups of patients with HFpEF with distinct clinical characteristics and long-term outcomes.

Phenomapping of Patients with Heart Failure with Preserved Ejection Fraction Using Machine Learning-Based Unsupervised Cluster Analysis

Structured PICO

Abstract

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Also Consider

Also Consider