Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.

Journal: BMC cardiovascular disorders

PMID: 32795252

Abstract

BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to identify patients who develop HFpEF.

Authors

Yvonne E Kaptein

Aurora Cardiovascular Services, Aurora St Luke's Medical Center, Milwaukee, WI, USA. yvonne.kaptein@gmail.com.
Ilya Karagodin

Department of Medicine, University of Chicago, Chicago, USA.
Hongquan Zuo

Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, USA.
Yu Lu

Faw-volkswagen Automative Co., Changchun, China.
Jun Zhang

First School of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
John S Kaptein

University of Southern California, Los Angeles, USA.
Jennifer L Strande

Department of Medicine, Medical College of Wisconsin, Milwaukee, USA.

Keywords

Aged Aged, 80 and over Asymptomatic Diseases Biomarkers Cluster Analysis Deep Learning Diagnosis, Computer-Assisted Diastole Disease Progression Echocardiography Female Heart Failure Humans Male Middle Aged Natriuretic Peptide, Brain Peptide Fragments Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Stroke Volume Unsupervised Machine Learning Ventricular Dysfunction, Left Ventricular Function, Left

External Resources

View on PubMed Access via DOI PubMed (32795252)

Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.

Abstract

Authors

Keywords

External Resources

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