Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices.

ABSTRACT:

Background

The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.

Methods

This retrospective study uses data from patients in the medical ICU from 2015-to-2019 to develop ML models. The models were based on dynamic ICU-readily available features such as hourly vital signs, laboratory results, and interventions and were developed using vari

SUBMITTER: Wu CP 

PROVIDER: S-EPMC11759737 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature