Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission

Acute kidney injury (AKI) is a prevalent and detrimental condition in intensive care unit patients. Most AKI predictive models only predict creatinine-triggered AKI (AKICr) and might underperform when predicting urine-output-triggered AKI (AKIUO). We aimed to describe how admission AKICr prediction...

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Veröffentlicht in:	Journal of critical care 2021-04, Vol.62, p.283-288
Hauptverfasser:	Schwager, Emma, Lanius, Stephanie, Ghosh, Erina, Eshelman, Larry, Pasupathy, Kalyan S., Barreto, Erin F., Kashani, Kianoush
Format:	Artikel
Sprache:	eng
Schlagworte:	Acute kidney injury Clinical decision support system Comorbidity Creatinine Critical care Machine learning Mortality Neural networks Patients Urine
Online-Zugang:	Volltext
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