Population stratification for prediction of mortality in post-AKI patients

TL;DR

Predictive models specialised in different categories of patients can increase accuracy of predictions in AKI, and some results following this approach are presented.

Abstract

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.

Figures (13)

• Figure 1: Circularity in optimised stratification of patients in groups
• Figure 2: Implementation execution workflow
• Figure 3: Decision diagrams -- synthetic data set
• Figure 4: Decision diagrams -- G-DaSH data set
• Figure 5: Group based gender distribution