Machine-Learning-Based Prediction Models for AKI

Acute Kidney Injury (AKI) is a common occurrence for critically ill patients in the ICU, and its early diagnosis has proven to be challenging. The accuracy of the online, machine-learning-based prediction model, AKIpredictor, was analysed for its use in a clinical setting. The study, which took place over five ICUs in Belgium, compared the predictions of AKIpredictor with physician predictions. The patient information for 250 individuals with no prior evidence of AKI or end-stage renal disease before ICU admission was used. Physicians then predicted AKI progression at three stages: at the initial admission, on the patient's first morning in the ICU and 24 hours later.