Academic Journal
Natural Language Processing to Identify Substance Use in Electronic Health Records: A Scoping Review.
| Title: | Natural Language Processing to Identify Substance Use in Electronic Health Records: A Scoping Review. |
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| Authors: | Azubuike CD; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA., Farrag A; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA., Zandbiglari K; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA., Soliman A; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA.; Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA., Bagchi C; Department of Microbiology and Cell Sciences, University of Florida, Gainesville, FL, USA., Mehrabi F; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA., Adkins LE; Health Science Center Libraries, University of Florida, Gainesville, FL, USA., Rouhizadeh M; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA., Goodin A; Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA. |
| Source: | Substance use & addiction journal [Subst Use Addctn J] 2026 Oct; Vol. 47 (4), pp. 1273-1287. Date of Electronic Publication: 2026 Jul 21. |
| Publication Type: | Journal Article; Scoping Review |
| Language: | English |
| Journal Info: | Publisher: Sage Publishing Country of Publication: United States NLM ID: 9918750589006676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2976-7350 (Electronic) Linking ISSN: 29767342 NLM ISO Abbreviation: Subst Use Addctn J Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Thousand Oaks, California : Sage Publishing, [2024]- |
| MeSH Terms: | Substance-Related Disorders*/diagnosis , Electronic Health Records* , Natural Language Processing*, Humans |
| Abstract: | Background: This scoping review aimed to characterize natural language processing (NLP) techniques deployed for identifying substance use in electronic health records (EHRs) and to compare the performance of these techniques by substance type. Methods: We conducted a systematic search of PubMed, the Cochrane Library, Embase, Web of Science, ACM Digital Library, IEEE Xplore, and Scopus for peer-reviewed original research published in English. Studies were eligible if they applied NLP to identify non-prescription or problematic substance use in EHRs, provided full-text access, and reported quantitative performance metrics. Results: A total of 86 studies met the inclusion criteria. NLP-identified substance use included tobacco (n = 42), alcohol (n = 26), non-prescription opioids (n = 30), cannabinoids (n = 9), stimulants (n = 6), and polysubstance use and/or other drugs (n = 16). NLP techniques included rule-based (n = 46), conventional machine learning (n = 39), deep learning (n = 17), and large language/transformer-based models (n = 22), with some studies applying multiple techniques. Annotation guidelines were available for 30 studies, and only 22 published their codes. Most studies reported performance metrics exceeding 0.80. Conclusions: Tobacco, opioids, and alcohol were the most frequently identified substances, whereas stimulants and cannabinoids were markedly underrepresented. Across the reviewed literature, transparency and reproducibility were limited, with few studies publishing code or detailed model specifications. Nevertheless, reported performance metrics were generally high. NLP techniques showed high performance in identifying tobacco, opioids, and alcohol use in EHRs, but stimulants and cannabinoids remain underrepresented. Transparency and reproducibility remain limited, underscoring the need for routine sharing of code, datasets, and model specifications. |
| Competing Interests: | Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. |
| Contributed Indexing: | Keywords: NLP techniques; electronic health records; natural language processing; substance abuse; substance-related disorders |
| Entry Date(s): | Date Created: 20260721 Date Completed: 20260915 Latest Revision: 20260922 |
| Update Code: | 20260922 |
| DOI: | 10.1177/29767342261463377 |
| PMID: | 42481076 |
| Database: | MEDLINE |
| ISSN: | 2976-7350 |
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| DOI: | 10.1177/29767342261463377 |