Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedd...
Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering
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Publisher
London: Nature Publishing Group UK
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Language
English
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Publisher
London: Nature Publishing Group UK
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Contents
Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk...
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Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering
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TN_cdi_doaj_primary_oai_doaj_org_article_7e1247669dad4ed396c906d967f51c73
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7e1247669dad4ed396c906d967f51c73
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-91297-x
