Predicting individual patient and hospital-level discharge using machine learning.

ABSTRACT:

Background

Accurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored.

Methods

We used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h. We fitted separate extreme gradient boosting models for elective and emergency admissions, trained on the first two years of data and tested on the final year of data. We examined individual-level and hospital-level model performance and evaluated the impact of training data size and recency, prediction time, and performance in subgroups.

Results

Our models achieve AUROCs of 0.87 and 0.

SUBMITTER: Wei J 

PROVIDER: S-EPMC11574281 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature