A Machine Learning Strategy to Predict the Number of High-Acuity Children Who Leave Without Being Seen From the Emergency Department
Machine learning models show excellent predictive performance in identifying optimal periods to operationally target and potentially mitigate the high-acuity LWBS phenomenon.
Authors
- Sarah Isbey
- Brandon Kappy (ORCID: https://orcid.org/0000-0001-7543-3925)
- Kenneth McKinley (ORCID: https://orcid.org/0000-0003-4624-5778)
- Marius George Linguraru (ORCID: https://orcid.org/0000-0001-6175-8665)
- Yiqi Su
- Patrick Butler
- James M. Chamberlain
- Naren Ramakrishnan
- Trang Ha (ORCID: https://orcid.org/0009-0007-9217-3150)
Institutions
- Children's National (US)
- George Washington University (US)
- Centre for Artificial Intelligence and Robotics (IN)
Publication Details
- Journal
- Journal of the American College of Emergency Physicians Open
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1016/j.acepjo.2025.100306
- Primary Topic
- Emergency and Acute Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00