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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Vydané v:	Medical sciences (Basel) Ročník 9; číslo 4; s. 60
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 24.09.2021 MDPI
Predmet:	acute kidney injury Acute Kidney Injury - diagnosis Adult Aged Aged, 80 and over AKI artificial intelligence Bicarbonates - blood Chlorides - blood Cluster Analysis clustering Consensus Creatinine Electronic health records Female Heart failure Hemoglobin Hospital Mortality Hospitalization Hospitals Humans Kidney diseases Laboratories Machine Learning Male Middle Aged Mortality nephrology Patients Potassium Variance analysis United States > US Minnesota nephrology hospitalization mortality acute kidney injury clustering machine learning artificial intelligence AKI
ISSN:	2076-3271, 2076-3271
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