Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

Journal: International journal of cardiology

Published Date: Jun 15, 2024

Abstract

BACKGROUND: Machine learning clustering of patients with ST-elevation acute myocardial infarction (STEMI) may provide important insights into their risk profile, management and prognosis.

Authors

Andrija Matetic

Department of Cardiology, University Hospital of Split, Split, Croatia; Keele Cardiovascular Research Group, Centre for Prognosis Research, Keele University, United Kingdom.
Theocharis Kyriacou

School of Computer Science and Mathematics, Keele University, Keele, United Kingdom.
Mamas A Mamas

Keele Cardiovascular Research Group, Centre for Prognosis Research, Keele University, United Kingdom; National Institute for Health and Care Research (NIHR), Birmingham Biomedical Research Centre, United Kingdom. Electronic address: mamasmamas1@yahoo.co.uk.

Keywords

Adult Aged Cluster Analysis Female Hospital Mortality Humans Machine Learning Male Middle Aged Prognosis Risk Factors ST Elevation Myocardial Infarction

External Resources

View on PubMed Access via DOI PubMed (38880421)

Machine-learning clustering analysis identifies novel phenogroups in patients with ST-elevation acute myocardial infarction.

Abstract

Authors

Keywords

External Resources

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