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Description Patient characteristics and clinical outcomes
Article Title: Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Publication Title: Critical Care -
Description Comparison of performance of AKIpredictor and physicians for prediction of AKI-23 by SCr and UO. The black dot represents the classification threshold from the physicians. a At ICU admission (n =...
Article Title: Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Publication Title: Critical Care -
Description Performance of AKIpredictor for predictions of AKI-23 by SCr and UO. a At ICU admission (n = 252), AUROC [95% CI] was 0.76 [0.66–0.85], net benefit in ranges 0–74%. b On the first morning of ICU...
Article Title: Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Publication Title: Critical Care -
Description Variables included in the different models of the AKIpredictor [19]
Article Title: Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Publication Title: Critical Care -
Description Flow chart
Article Title: Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Publication Title: Critical Care -
Description Illustration of the decision curve analysis. The example illustrates the decision curve of a model to predict whether patients will have AKI, from a population with an AKI prevalence of 9%. In the...
Article Title: Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor
Publication Title: Critical Care
Displaying all 6 assets.