ArticlePharmacology research & perspectives2020
Development of a machine learning algorithm for early detection of opioid use disorder.
Article in Pharmacology research & perspectives, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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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.
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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
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Evidence-based framework for identifying opioid use disorder in administrative data: A systematic review and methodological development study.Pain medicine (Malden, Mass.) · 2026Pooled it
- Predictive Models for Identifying Adult Patients at High Risk of Developing Opioid-Related Harms: a Systematic Review.Drug safety · 2025Pooled it
- Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.Journal of addiction medicinePooled it
- Developing and Validating a Machine Learning Algorithm to Predict the Risk of Incident Opioid Use Disorder Among OneFlorida+ Patients: Prognostic Modeling Study.Journal of medical Internet research · 2026Article
- Association Between Spinal Manipulative Therapy for Low Back Pain With or Without Sciatica and Opioid Use Disorder: A Retrospective Cohort Study.Health science reports · 2025Article
- Functional brain network identification in opioid use disorder using machine learning analysis of resting-state fMRI BOLD signals.Computers in biology and medicine · 2025Article
- Detecting Opioid Use Disorder in Health Claims Data With Positive Unlabeled Learning.IEEE journal of biomedical and health informatics · 2025Article
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Discovering opioid slang on social media: A Word2Vec approach with reddit data.Drug and alcohol dependence reports · 2024Article
- Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.Journal of personalized medicine · 2024Review
- Machine Learned Classification of Ligand Intrinsic Activities at Human μ-Opioid Receptor.ACS chemical neuroscience · 2024Article
- Machine Learned Classification of Ligand Intrinsic Activities at HumanbioRxiv : the preprint server for biology · 2024Article
- Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities.Addiction (Abingdon, England) · 2024Observational
- Individualized Prospective Prediction of Opioid Use Disorder.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2023Article
- Opioid-related harms and care impacts of conventional and AI-based prescription management strategies: insights from leveraging agent-based modeling and machine learning.Frontiers in digital health · 2023Article
- The evolution of Big Data in neuroscience and neurology.Journal of big data · 2023Article
- Identifying Opioid Use Disorder from Longitudinal Healthcare Data using a Multi-stream Transformer.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Development of a machine learning algorithm for early detection of opioid use disorder.Pharmacology research & perspectives · 2020Article
- Predicting opioid use disorder before and after the opioid prescribing peak in the United States: A machine learning tool using electronic healthcare records.Health informatics journalArticle
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOpioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD. SUBJECTS AND
methodsWe analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550 000 patient records. We compiled 436 predictor candidates, divided to six feature groups - demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis.
resultsThe c-statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD- and negative OUD- controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder-related complaints per year, post laminectomy syndrome diagnosed per year, and pain disorders diagnosis per year. Significant differences were also found in the episodes and costs categories.
conclusionsThe new algorithm offers a mean 14.4 months reduction in time to diagnosis of OUD, at potential saving in further morbidity, medical cost, addictions and mortality.
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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.