ArticleJournal of biomedical informatics2019
Machine learning for phenotyping opioid overdose events.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis 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.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Clinicians' and people who use drugs' perspectives on artificial intelligence in addiction medicine.Addiction (Abingdon, England) · 2026Article
- Artificial Intelligence for Opioid Safety Surveillance from Clinical Text: A Clinically Focused Review.Journal of clinical medicine · 2026Review
- Efficacy and safety of Oliceridine versus Sufentanil in postoperative analgesia for burn skin grafting: a machine learning and SHAP-based cohort study.Frontiers in medicine · 2026Article
- Leveraging data analytics to enhance public health system performance in Peru.Frontiers in public health · 2026Review
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Identifying emergency department patients at high risk for opioid overdose using natural language processing and machine learning.Journal of substance use and addiction treatment · 2025Article
- Modeling opioid overdose events recurrence with a covariate-adjusted triggering point process.PLoS computational biology · 2025Article
- Identifying Opioid Overdose and Opioid Use Disorder and Related Information from Clinical Narratives Using Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Article
- Question-answering system extracts information on injection drug use from clinical notes.Communications medicine · 2024Article
- LATTE: Label-efficient incident phenotyping from longitudinal electronic health records.Patterns (New York, N.Y.) · 2024Article
- Enhanced phenotypes for identifying opioid overdose in emergency department visit electronic health record data.JAMIA open · 2023Article
- Benchmarking Transformer-Based Models for Identifying Social Determinants of Health in Clinical Notes.Proceedings. IEEE International Conference on Healthcare Informatics · 2023Article
- Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures.BMC medical informatics and decision making · 2023Article
- Machine learning approaches for electronic health records phenotyping: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Computational phenotypes for patients with opioid-related disorders presenting to the emergency department.PloS one · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Narrative Review of Machine Learning in Rheumatic and Musculoskeletal Diseases for Clinicians and Researchers: Biases, Goals, and Future Directions.The Journal of rheumatology · 2022Review
- Natural Language Processing and Machine Learning to Identify People Who Inject Drugs in Electronic Health Records.Open forum infectious diseases · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop machine learning models for classifying the severity of opioid overdose events from clinical data. MATERIALS AND
methodsOpioid overdoses were identified by diagnoses codes from the Marshfield Clinic population and assigned a severity score via chart review to form a gold standard set of labels. Three primary feature sets were constructed from disparate data sources surrounding each event and used to train machine learning models for phenotyping.
resultsRandom forest and penalized logistic regression models gave the best performance with cross-validated mean areas under the ROC curves (AUCs) for all severity classes of 0.893 and 0.882 respectively. Features derived from a common data model outperformed features collected from disparate data sources for the same cohort of patients (AUCs 0.893 versus 0.837, p value = 0.002). The addition of features extracted from free text to machine learning models also increased AUCs from 0.827 to 0.893 (p value < 0.0001). Key word features extracted using natural language processing (NLP) such as 'Narcan' and 'Endotracheal Tube' are important for classifying overdose event severity.
conclusionRandom forest models using features derived from a common data model and free text can be effective for classifying opioid overdose events.
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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.