Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments

Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments

Objective Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different sites. Our objective was to develop interpretable ML mo...

Full description

Saved in:
Bibliographic Details
Main Authors: Abhishek Sharma, Timothy N Fazio, Long Song, Samantha Plumb, Uwe Aickelin, Mojgan Kouhounestani, Mark John Putland
Format: Article
Language:English
Published: BMJ Publishing Group 2025-06-01
Series:BMJ Health & Care Informatics
Online Access:https://informatics.bmj.com/content/32/1/e101152.full
Tags: Add Tag
No Tags, Be the first to tag this record!
Login for hold and recall information

Internet

https://informatics.bmj.com/content/32/1/e101152.full

Similar Items