Stratified Mortality Prediction of Patients with Acute Kidney Injury in Critical Care.

Autor:	Xu Z; Weill Cornell Medicine, Cornell University, New York, New York, USA., Luo Y; Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA., Adekkanattu P; Weill Cornell Medicine, Cornell University, New York, New York, USA., Ancker JS; Weill Cornell Medicine, Cornell University, New York, New York, USA., Jiang G; Mayo Clinic, Rochester, Minnesota, USA., Kiefer RC; Mayo Clinic, Rochester, Minnesota, USA., Pacheco JA; Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA., Rasmussen LV; Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA., Pathak J; Weill Cornell Medicine, Cornell University, New York, New York, USA., Wang F; Weill Cornell Medicine, Cornell University, New York, New York, USA.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2019 Aug 21; Vol. 264, pp. 462-466.
DOI:	10.3233/SHTI190264
Abstrakt:	Acute Kidney Injury (AKI) is the most common cause of organ dysfunction in critically ill adults and prior studies have shown AKI is associated with a significant increase of the mortality risk. Early prediction of the mortality risk for AKI patients can help clinical decision makers better understand the patient condition in time and take appropriate actions. However, AKI is a heterogeneous disease and its cause is complex, which makes such predictions a challenging task. In this paper, we investigate machine learning models for predicting the mortality risk of AKI patients who are stratified according to their AKI stages. With this setup we demonstrate the stratified mortality prediction performance of patients with AKI is better than the results obtained on the mixed population.
Databáze:	MEDLINE
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