ArticleFrontiers in public health2022
Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Implementation of an opioid use disorder (OUD) machine-learning phenotype in real-time for the ADAPT clinical trial.PLOS digital health · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project.medRxiv : the preprint server for health sciences · 2025Article
- Sensitivity and Specificity of Natural Language Processing Systems for Identification of Hospitalized People Who Use Drugs.Open forum infectious diseases · 2025Article
- Diagnosis and coding of opioid misuse: a systematic scoping review and implementation framework.Pain medicine (Malden, Mass.) · 2025Article
- One Third of Alcohol Use Disorder Diagnoses are Missed by ICD Coding.Substance use & addiction journal · 2025Article
- Decoding substance use disorder severity from clinical notes using a large language model.Npj mental health research · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 2025Article
- Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.JMIR medical informatics · 2024Article
- A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes.Healthcare (Basel, Switzerland) · 2024Article
- Question-answering system extracts information on injection drug use from clinical notes.Communications medicine · 2024Article
- Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.JMIR mental health · 2024Article
- Identification of opioid use disorder using electronic health records: Beyond diagnostic codes.Drug and alcohol dependence · 2023Article
- Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records.Open forum infectious diseases · 2022Article
- Novel digital approaches to the assessment of problematic opioid use.BioData mining · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Background: Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and social risks and behaviors. To capture this information at scale, natural language processing (NLP) tools must be developed and evaluated. We developed and applied an annotation schema to deeply characterize OUD and related clinical, behavioral, and environmental factors, and automated the annotation schema using machine learning and deep learning-based approaches. Methods: Using the MIMIC-III Critical Care Database, we queried hospital discharge summaries of patients with Results: The resulting annotation schema contained 32 classes. We achieved moderate inter-annotator agreement, with F Conclusion: This pilot study demonstrated that rich information regarding problematic opioid use can be manually identified by annotators. However, more training samples and features would improve our ability to reliably identify less common classes from clinical text, including text from outpatient settings.
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