Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation
Open accessAbstract
Objective: Alcohol misuse is present in over a quarter of trauma patients. Information in the clinical notes of the electronic health record of trauma patients may be used for phenotyping tasks with natural language processing (NLP) and supervised machine learning. The objective of this study is to train and validate an NLP classifier for identifying patients with alcohol misuse. Materials and Methods: An observational cohort of 1422 adult patients admitted to a trauma center between April 2013 and November 2016. Linguistic processing of clinical notes was performed using the clinical Text Analysis and Knowledge Extraction System. The primary analysis was the binary classification of alcohol misuse. The Alcohol Use Disorders Identification Test served as the reference standard. Results: The data corpus comprised 91 045 electronic health record notes and 16 091 features. In the final machine learning classifier, 16 features were selected from the first 24 hours of notes for identifying alcohol misuse. The classifier’s performance in the validation cohort had an area under the receiver-operating characteristic curve of 0.78 (95% confidence interval [CI], 0.72 to 0.85). Sensitivity and specificity were at 56.0% (95% CI, 44.1% to 68.0%) and 88.9% (95% CI, 84.4% to 92.8%). The Hosmer-Lemeshow goodness-of-fit test demonstrates the classifier fits the data well (P = .17). A simpler rule-based keyword approach had a decrease in sensitivity when compared with the NLP classifier from 56.0% to 18.2%. Conclusions: The NLP classifier has adequate predictive validity for identifying alcohol misuse in trauma centers. External validation is needed before its application to augment screening.
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Most methods for SDoH extraction were developed and evaluated within specific healthcare systems.
“Thirdly, most methods were developed and evaluated within specific healthcare systems, such as a Veterans Health Administration center35, a Medical Center36, and a Trauma Center37.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [37]
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- Authors
- Afshar, M., Phillips, A., Karnik, N., Mueller, J., To, D., Gonzalez, R., Price, R., Cooper, R., Joyce, C., & Dligach, D.
- Affiliations
- Loyola University Chicago (US)
- Rush University Medical Center (US)
- Loyola University Medical Center (US)
- Corresponding author
- Afshar, Majid
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-01-02
- Volume
- 26
- Issue
- 3
- Pages
- 254-261
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC6657384
- OpenAlex
- W2908358765
- Cited by
- 94
- References
- 36
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 12 · 2023: 12 · 2022: 14 · 2021: 18 · 2020: 8 · 2019: 5
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Civil War Book Review (submittedVersion)
- Figshare (submittedVersion, cc-by)
- University of Illinois Chicago
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- alcohol abuse, trauma patients, electronic health records, natural language processing, clinical notes, phenotyping, Alcohol Use Disorders Identification Test, alcohol screening, clinical text mining
- MeSH terms
- Machine Learning, Adult, Alcoholism, Female, Humans, Male, Middle Aged, Natural Language Processing, ROC Curve, Trauma Centers, Wounds and Injuries, Cohort Studies, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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