ArticleAlcohol (Fayetteville, N.Y.)2020
Validation of an alcohol misuse classifier in hospitalized patients.
Article in Alcohol (Fayetteville, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Impact of Alcohol Misuse on Requirements for Critical Care Services and Development of Hospital Delirium in Patients With COVID-19 pneumonia.Critical care explorations · 2023Article
- External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.Addiction (Abingdon, England) · 2022Article
- Governance of Clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes.Frontiers in digital health · 2022Article
- Investigating Unhealthy Alcohol Use As an Independent Risk Factor for Increased COVID-19 Disease Severity: Observational Cross-sectional Study.JMIR public health and surveillance · 2021Observational
- External validation of an opioid misuse machine learning classifier in hospitalized adult patients.Addiction science & clinical practice · 2021Observational
- Bias Assessment and Correction in Machine Learning Algorithms: A Use-Case in a Natural Language Processing Algorithm to Identify Hospitalized Patients with Unhealthy Alcohol Use.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- The Addition of United States Census-Tract Data Does Not Improve the Prediction of Substance Misuse.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Multimodal Prediction of Obsessive-Compulsive Disorder and Comorbid Depression Severity and Energy Delivered by Deep Brain Electrodes.IEEE transactions on affective computingArticle
- 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
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundCurrent modes of identifying alcohol misuse in hospitalized patients rely on self-report questionnaires and diagnostic codes that have limitations, including low sensitivity. Information in the clinical notes of the electronic health record (EHR) may further augment the identification of alcohol misuse. Natural language processing (NLP) with supervised machine learning has been successful at analyzing clinical notes and identifying cases of alcohol misuse in trauma patients.
methodsAn alcohol misuse NLP classifier, previously developed on trauma patients who completed the Alcohol Use Disorders Identification Test, was validated in a cohort of 1000 hospitalized patients at a large, tertiary health system between January 1, 2007 and September 1, 2017. The clinical notes were processed using the clinical Text Analysis and Knowledge Extraction System. The National Institute on Alcohol Abuse and Alcoholism (NIAAA) guidelines for alcohol misuse were used during annotation of the medical records in our validation dataset.
resultsThe alcohol misuse classifier had an area under the receiver operating characteristic curve of 0.91 (95% CI 0.90-0.93) in the cohort of hospitalized patients. The sensitivity, specificity, positive predictive value, and negative predictive value were 0.88 (95% CI 0.85-0.90), 0.78 (95% CI 0.74-0.82), 0.85 (95% CI 0.82-0.87), and 0.82 (95% CI 0.78-0.86), respectively. The Hosmer-Lemeshow Test (p = 0.13) demonstrates good model fit. Additionally, there was a dose-dependent response in alcohol consumption behaviors across increasing strata of predicted probabilities for alcohol misuse.
conclusionThe alcohol misuse NLP classifier had good discrimination and test characteristics in hospitalized patients. An approach using the clinical notes with NLP and supervised machine learning may better identify alcohol misuse cases than conventional methods solely relying on billing diagnostic codes.
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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.