Distinct Subtypes of Hepatorenal Syndrome and Associated Outcomes as Identified by Machine Learning Consensus Clustering

Background: 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...

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Detalles Bibliográficos
Autores principales: Tangpanithandee, Supawit, Thongprayoon, Charat, Krisanapan, Pajaree, Mao, Michael A., Kaewput, Wisit, Pattharanitima, Pattharawin, Boonpheng, Boonphiphop, Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944494/
https://www.ncbi.nlm.nih.gov/pubmed/36810532
http://dx.doi.org/10.3390/diseases11010018

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944494/
https://www.ncbi.nlm.nih.gov/pubmed/36810532
http://dx.doi.org/10.3390/diseases11010018

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