Population stratification for prediction of mortality in post-AKI patients
This work addresses mortality risk prediction for hospitalized patients with acute kidney injury, but it appears incremental as it builds on existing predictive modeling approaches.
The study tackled the problem of predicting mortality in post-AKI patients by developing specialized predictive models for different patient categories, which increased prediction accuracy, though no concrete numbers were provided.
Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare expenditures can be minimised by followup planning grounded on predictive models and machine learning. Since AKI is multi-factorial, predictive models specialised in different categories of patients can increase accuracy of predictions. In the present article we present some results following this approach.