Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning...
Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering
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Publisher
Switzerland: MDPI AG
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Language
English
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Publisher
Switzerland: MDPI AG
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Contents
The utilization of multi-dimensional patient data to subtype hepatorenal syndrome (HRS) can individualize patient care. Machine learning (ML) consensus clustering may identify HRS subgroups with unique clinical profiles. In this study, we aim to identify clinically meaningful clusters of hospitalized patients for HRS using an unsupervised ML cluste...
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Full title
Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering
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TN_cdi_doaj_primary_oai_doaj_org_article_8811293708fd4063acc0b869037c9e2c
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_8811293708fd4063acc0b869037c9e2c
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ISSN
2079-9721
E-ISSN
2079-9721
DOI
10.3390/diseases11010018
