ArticleJournal of the American Medical Informatics Association : JAMIA2021
Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.Journal of addiction medicinePooled it
- Responsible AI for safer opioid risk management in older adults.npj health systems · 2026Article
- A machine learning approach for opioid overdose risk prediction among Alabama Medicaid beneficiaries with opioid prescriptions.Scientific reports · 2026Article
- Predicting Fatal Drug Poisoning Among People Living with HIV-HCV Co-Infection.Canadian liver journal · 2025Article
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.Bioelectronic medicine · 2024Article
- A Treatment Selection Model for Opioid Use Disorder Using Electronic Health Record and ZIP-Level Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Opioid death projections with AI-based forecasts using social media language.NPJ digital medicine · 2023Article
- Predicting premature discontinuation of medication for opioid use disorder from electronic medical records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Using machine learning to study the effect of medication adherence in Opioid Use Disorder.PloS one · 2022Article
- Predictive models: important problems and innovative methods.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectiveTo develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication. MATERIALS AND
methodsStudy data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Statewide data and socioeconomic indicators were used to train, ensemble, and calibrate 10 nonparametric "weak learner" models. Validation was performed using area under the receiver operating curve (AUROC), area under the precision recall curve, risk concentration, and Spiegelhalter z-test statistic.
resultsWithin 30 days, 2574 fatal overdoses occurred after 4912 prescriptions (0.0069%) and 8455 nonfatal overdoses occurred after 19 460 prescriptions (0.027%). Discrimination and calibration improved after ensembling (AUROC: 0.79-0.83; Spiegelhalter P value: 0-.12). Risk concentration captured 47-52% of cases in the top quantiles of predicted probabilities. DISCUSSION: Partitioning and ensembling enabled all study data to be used given computational limits and helped mediate case imbalance. Predicting risk at the prescription level can aggregate risk to the patient, provider, pharmacy, county, and regional levels. Implementing these models into Tennessee Department of Health systems might enable more granular risk quantification. Prospective validation with more recent data is needed.
conclusionPredicting opioid-related overdose risk at statewide scales remains difficult and models like these, which required a partnership between an academic institution and state health agency to develop, may complement traditional epidemiological methods of risk identification and inform public health decisions.
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