ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Using Natural Language Processing and Machine Learning to Identify Hospitalized Patients with Opioid Use Disorder.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 26 citations in OpenAlex.
- Extended-release buprenorphine treatment for opioid use disorder: A mixed-methods study of response and experience.Addiction (Abingdon, England) · 2025Trial
- Examining Ethnic and Racial Variations in Opioid-related Hospitalizations Using Western U.S. Electronic Health Records.Journal of racial and ethnic health disparities · 2026Review
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Transforming Opioid Poisoning Surveillance Through Novel Technologies: Rationale and Methodological Protocol for Applying Natural Language Processing to Emergency Department Data.Drug and alcohol review · 2026Article
- Enhancing substance use detection in clinical notes with large language models.Drug and alcohol dependence · 2025Article
- Unveiling social determinants of health impact on adverse pregnancy outcomes through natural language processing.Scientific reports · 2025Article
- Speak and You Shall Predict: Evidence That Speech at Initial Cocaine Abstinence Is a Biomarker of Long-Term Drug Use Behavior.Biological psychiatry · 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
- Enhancing Substance Use Detection in Clinical Notes with Large Language Models.Research square · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 2025Article
- A deep learning analysis for dual healthcare system users and risk of opioid use disorder.Scientific reports · 2025Article
- A Review of Leveraging Artificial Intelligence to Predict Persistent Postoperative Opioid Use and Opioid Use Disorder and its Ethical Considerations.Current pain and headache reports · 2025Review
- ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 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
- Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.Journal of registry management · 2024Article
- Natural Language Processing-Assisted Classification Models to Confirm Monoclonal Gammopathy of Undetermined Significance and Progression in Veterans' Electronic Health Records.JCO clinical cancer informatics · 2023Article
- Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults.JMIR medical informatics · 2023Article
- Classifying Firearm Injury Intent in Electronic Hospital Records Using Natural Language Processing.JAMA network open · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Opioid use disorder (OUD) represents a global public health crisis that challenges classic clinical decision making. As existing hospital screening methods are resource-intensive, patients with OUD are significantly under-detected. An automated and accurate approach is needed to improve OUD identification so that appropriate care can be provided to these patients in a timely fashion. In this study, we used a large-scale clinical database from Mass General Brigham (MGB; formerly Partners HealthCare) to develop an OUD patient identification algorithm, using multiple machine learning methods. Working closely with an addiction psychiatrist, we developed a set of hand-crafted rules for identifying information suggestive of OUD from free-text clinical notes. We implemented a natural language processing (NLP)-based classification algorithm within the Medical Text Extraction, Reasoning and Mapping System (MTERMS) tool suite to automatically label patients as positive or negative for OUD based on these rules. We further used the NLP output as features to build multiple machine learning and a neural classifier. Our methods yielded robust performance for classifying hospitalized patients as positive or negative for OUD, with the best performing feature set and model combination achieving an F1 score of 0.97. These results show promise for the future development of a real-time tool for quickly and accurately identifying patients with OUD in the hospital setting.
Indexed as
Identifiers
33936395PMC8075424W3157176745What OpenQuestion holds
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.