Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographi...

Descripción completa

Detalles Bibliográficos
Autores principales: Thongprayoon, Charat, Vaitla, Pradeep, Nissaisorakarn, Voravech, Mao, Michael A., Genovez, Jose L. Zabala, Kattah, Andrea G., Pattharanitima, Pattharawin, Vallabhajosyula, Saraschandra, Keddis, Mira T., Qureshi, Fawad, Dillon, John J., Garovic, Vesna D., Kashani, Kianoush B., Cheungpasitporn, Wisit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8544570/
https://www.ncbi.nlm.nih.gov/pubmed/34698185
http://dx.doi.org/10.3390/medsci9040060

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8544570/
https://www.ncbi.nlm.nih.gov/pubmed/34698185
http://dx.doi.org/10.3390/medsci9040060

Ejemplares similares