The ENACT network is acting on housing instability and the unhoused using the open health natural language processing toolkit.

Autor:	Harris DR; Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY, USA.; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA., Fu S; Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA., Wen A; Center for Translational AI Excellence and Applications in Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA., Corbeau A; Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY, USA.; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA., Henderson D; Center for Clinical and Translational Sciences, University of Kentucky, Lexington, KY, USA.; Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA., Hilsman J; Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA., Oniani D; Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA., Wang Y; Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.; Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Jazyk:	angličtina
Zdroj:	Journal of clinical and translational science [J Clin Transl Sci] 2024 May 16; Vol. 8 (1), pp. e98. Date of Electronic Publication: 2024 May 16 (Print Publication: 2024).
DOI:	10.1017/cts.2024.543
Abstrakt:	Housing is an environmental social determinant of health that is linked to mortality and clinical outcomes. We developed a lexicon of housing-related concepts and rule-based natural language processing methods for identifying these housing-related concepts within clinical text. We piloted our methods on several test cohorts: a synthetic cohort generated by ChatGPT for initial infrastructure testing, a cohort with substance use disorders (SUD), and a cohort diagnosed with problems related to housing and economic circumstances (HEC). Our methods successfully identified housing concepts in our ChatGPT notes (recall = 1.0, precision = 1.0), our SUD population (recall = 0.9798, precision = 0.9898), and our HEC population (recall = N/A, precision = 0.9160). Competing Interests: None. (© The Author(s) 2024.)
Databáze:	MEDLINE
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