Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients w...
Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure
About this item
Full title
Author / Creator
Publisher
England: John Wiley & Sons, Inc
Journal title
Language
English
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Publication information
Publisher
England: John Wiley & Sons, Inc
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Scope and Contents
Contents
Aims
Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previous...
Alternative Titles
Full title
Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure
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TN_cdi_doaj_primary_oai_doaj_org_article_1b9e090feb37462b83ab56d1a4cf54a0
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_1b9e090feb37462b83ab56d1a4cf54a0
Other Identifiers
ISSN
2055-5822
E-ISSN
2055-5822
DOI
10.1002/ehf2.15027
