ArticleAddiction (Abingdon, England)2022
External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.
Article in Addiction (Abingdon, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing.Drug and alcohol dependence · 2025Article
- Alcohol use-related problems in general hospitals and primary care settings: Screening, intervention, and referral to treatment.World journal of psychiatry · 2025Review
- Development of New Diagnostic Techniques: Machine Learning.Advances in experimental medicine and biology · 2025Review
- Performance of International Classification of Disease-10 codes in detecting emergency department patients with opioid misuse.Addiction (Abingdon, England) · 2024Article
- Automated-detection of risky alcohol use prior to surgery using natural language processing.Alcohol, clinical & experimental research · 2024Article
- Article
- Commentary on Lin et al.: The importance of valid reference standards in training supervised machine learning classifiers to detect alcohol misuse.Addiction (Abingdon, England) · 2022Article
- External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.Addiction (Abingdon, England) · 2022Article
- Using a natural language processing toolkit to classify electronic health records by psychiatric diagnosis.Health informatics journalArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
aimsUnhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center.
designRetrospective cohort study.
settingThe site for validation was a midwestern United States tertiary-care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service. PARTICIPANTS/CASES: Unplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605). MEASUREMENTS: The Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under-reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT-derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis.
findingsUsing clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision-recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89-0.92) and 0.56 (95% CI = 0.53-0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62-0.69), 0.98 (95% CI = 0.98-0.98), 0.35 (95% CI = 0.33-0.38), and 1.0 (95% CI = 1.0-1.0), respectively.
conclusionsExternal validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at-risk patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.