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- Title
Identifying incarceration status in the electronic health record using large language models in emergency department settings.
- Authors
Huang, Thomas; Socrates, Vimig; Gilson, Aidan; Safranek, Conrad; Ling Chi; Wang, Emily A.; Puglisi, Lisa B.; Brandt, Cynthia; Taylor, R. Andrew; Wang, Karen
- Abstract
This article explores the use of natural language processing (NLP) techniques to identify incarceration status in electronic health records (EHR) in emergency department settings. The study developed and validated NLP models to accurately identify incarceration status from clinical notes, which could potentially help address health disparities and tailor interventions for individuals with a history of incarceration. The study found that the Clinical-Longformer NLP model was effective in accurately identifying incarceration history, although it had limitations in distinguishing temporal labels. It is important to approach patients in a non-judgmental manner and confirm incarceration history before offering resources to avoid stigma. The NLP model can serve as a screening tool, but manual confirmation is recommended to avoid false positives or misplacements of electronic labels in patient files.
- Publication
Journal of Clinical & Translational Science, 2024, Vol 8, Issue 1, p1
- ISSN
2059-8661
- Publication type
Article
- DOI
10.1017/cts.2024.496