Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario

Applying our algorithm to MIMIC data generated an AUROC of 0.821 (95% CI 0.820-0.821) per blood test. Our cancer derived algorithm compares positively with other AKI models derived and/or tested on MIMIC, with our model predicting AKI at the longest time frame of up to 30 days. This suggests that our model can achieve a good performance on patient cohorts very different to those from which it was derived, demonstrating the transferability and applicability for implementation in a clinical setting.