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...

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Bibliographic Details

Published in:	Medical Sciences
Main Authors:	Charat Thongprayoon, Pradeep Vaitla, Voravech Nissaisorakarn, Michael A. Mao, Jose L. Zabala Genovez, Andrea G. Kattah, Pattharawin Pattharanitima, Saraschandra Vallabhajosyula, Mira T. Keddis, Fawad Qureshi, John J. Dillon, Vesna D. Garovic, Kianoush B. Kashani, Wisit Cheungpasitporn
Format:	Article
Language:	English
Published:	MDPI AG 2021-09-01
Subjects:	acute kidney injury AKI clustering machine learning nephrology artificial intelligence
Online Access:	https://www.mdpi.com/2076-3271/9/4/60