Fig. 1

shows an example patient’s AKI disease trajectory and uses the prediction made 36 h before KDIGO Stage 2/3 AKI onset to demonstrate the inner-workings of the model. The top plot shows the patient’s measured serum creatinine values, with AKI onset time referenced as Time 0. The middle plot shows the predicted AKI risk up to prediction time—36 h before AKI onset. It also shows a mockup of the AKI alert that a user would see, including the patient context and suggested actions. The top three predictors contributing the highest risk to this specific prediction are displayed. The bottom portion demonstrates that the model is made up of age-dependent ‘weak classifiers’ of AKI risk based on single predictor values. The predicted AKI risk is the sum of weak classifier risks of all input predictors. Two example weak classifiers are shown. The first is the classifier for creatinine rate of change (CRoC). In the top plot, the example patient’s serum creatinine increases slowly under the AKI threshold prior to prediction time. The increase results in a positive CRoC value and elevated CRoC weak classifier risk of 0.60, as marked on the CRoC classifier plot. At the same time, the patient continuously received drugs with high nephrotoxic potential, shown by triangular ticks marking times of medication administration in the top plot. This results in the high-nephrotoxic drugs classifier risk being elevated to 0.14 (bottom plot). The ellipses (…) in the figure are placeholders for additional predictors not shown due to room constraints