Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines.

ABSTRACT:

Background

Intensive care unit (ICU) readmissions are associated with mortality and poor outcomes. To improve discharge decisions, machine learning (ML) could help to identify patients at risk of ICU readmission. However, as many models are black boxes, dangerous properties may remain unnoticed. Widely used post hoc explanation methods also have inherent limitations. Few studies are evaluating inherently interpretable ML models for health care and involve clinicians in inspecting the trained model.

Methods

An inherently interpretable model for the prediction of 3 day ICU readmission was developed. We used explainable boosting machines that learn modular risk functions and which have already been shown to be suitable for the health care domain. We created a retrospecti

SUBMITTER: Hegselmann S 

PROVIDER: S-EPMC9445989 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature