Interpretable machine learning models for prolonged Emergency Department wait time prediction
Nearly half of the patients experienced prolonged wait times in the ED. ML models demonstrated acceptable performance, particularly in minimizing FNR when predicting ED wait times. The prediction of prolonged wait times was influenced by multiple interacting factors. Proper application of ML models to clinical practice requires interpreting their predictions of prolonged wait times in the context of clinical significance.
Authors
- Hao Wang (ORCID: https://orcid.org/0000-0002-5105-0951)
- Nethra Sambamoorthi
- Usha Sambamoorthi (ORCID: https://orcid.org/0000-0001-8311-1360)
- Devin Sandlin
Institutions
- University of North Texas (US)
- University of North Texas Health Science Center (US)
- John Peter Smith Hospital (US)
- Health Net (US)
Publication Details
- Journal
- BMC Health Services Research
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1186/s12913-025-12535-w
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- National Institute on Minority Health and Health Disparities