$Phenomapping of patients with heart failure with preserved ejection fraction using machine learning‐based unsupervised cluster analysis$

Phenomapping of patients with heart failure with preserved ejection fraction using machine learning‐based unsupervised cluster analysis

Aim To identify distinct phenotypic subgroups in a highly‐dimensional, mixed‐data cohort of individuals with heart failure (HF) with preserved ejection fraction (HFpEF) using unsupervised clustering analysis. Methods and results The study included all Treatment of Preserved Cardiac Function Heart Fa...

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Veröffentlicht in:	European journal of heart failure 2020-01, Vol.22 (1), p.148-158
Hauptverfasser:	Segar, Matthew W., Patel, Kershaw V., Ayers, Colby, Basit, Mujeeb, Tang, W.H. Wilson, Willett, Duwayne, Berry, Jarett, Grodin, Justin L., Pandey, Ambarish
Format:	Artikel
Sprache:	eng
Schlagworte:	Cluster Analysis Heart Failure - epidemiology Heart failure with preserved ejection fraction Humans Machine Learning Mineralocorticoid Receptor Antagonists Outcomes Phenomapping Prognosis Stroke Volume
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