Observational studyJournal of the American Medical Informatics Association : JAMIA2019
Natural language processing and machine learning to identify alcohol misuse from the electronic health record in trauma patients: development and internal validation.
Observational study in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03833804 (Data-driven Strategies for Substance Misuse Identification in Hospitalized Patients), which is not on this map. Cited by 52 papers, 6 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Data-driven Strategies for Substance Misuse Identification in Hospitalized Patients
Who cites it
52 citing papers in PubMed, 6 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- The relationship between language features and PTSD symptoms: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- 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
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Musculoskeletal Clinicians' Perspectives on Integrated Digital Personalized Feedback Interventions to Address Risky Alcohol use and PTSD after Musculoskeletal Injury.Journal of clinical psychology in medical settings · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Unhealthy alcohol use detection in electronic health records: A comparative study using natural language processing.Drug and alcohol dependence · 2025Article
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 2025Article
- On the use of natural language processing to implement the target trial framework using unstructured data from the electronic health record.Global epidemiology · 2025Article
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review.International journal of mental health nursing · 2025Review
- Automated detection of cervical spondylotic myelopathy: harnessing the power of natural language processing.Frontiers in neuroscience · 2025Article
- Perspective on Using Artificial Intelligence in Alcohol Research and Treatment: Opportunities and Ethical Considerations.Alcohol research : current reviews · 2025Review
- Using Large Language Models to Extract Core Injury Information From Emergency Department Notes.Journal of Korean medical science · 2024Article
- The Impact of Collaborative Documentation on Person-Centered Care: Textual Analysis of Clinical Notes.JMIR medical informatics · 2024Article
- Identifying Self-Disclosures of Use, Misuse and Addiction in Community-based Social Media Posts.Findings of ACL. NAACL · 2024Article
- High-density lipoprotein (HDL) as an indicator for alcohol use in a psychiatrically ill population.Alcohol and alcoholism (Oxford, Oxfordshire) · 2024Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
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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Registered trials
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.