ArticleNPJ digital medicine2025
A systematic review of machine learning applications in predicting opioid associated adverse events.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- A hierarchical clinical fusion transformer model for personalized opioid treatment: Development and validation in diabetic surgical patients.medRxiv : the preprint server for health sciences · 2026Article
- Article
- AI-driven quantum chemical exploration of multi-target synthetic opioid analogs for enhanced therapeutic profiles.Journal of computer-aided molecular design · 2026Article
- Ethical challenges and opportunities for integrating predictive analytics in community-based overdose prevention.Lancet regional health. Americas · 2026Review
- Comparative risk of delirium among opioid users for non-cancer pain: a retrospective cohort study.BMC medicine · 2026Article
- A machine learning approach for opioid overdose risk prediction among Alabama Medicaid beneficiaries with opioid prescriptions.Scientific reports · 2026Article
- Development and evaluation of machine learning algorithms for the prediction of opioid-related deaths among UK patients with non-cancer pain.PLOS digital health · 2026Article
- HIBERT: A Hybrid Clustering BERT for Interpretable Opioid Overdose Risk Prediction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions. Most studies (96%) originated from North America, with only 7% reporting external validation. Model performance was moderate to strong, but calibration was often missing (41%). Transparent reporting of model development was often incomplete, with key aspects such as calibration, imbalance correction, and handling of missing data absent. Infrequent external validation limited the generalizability of current models. Addressing these aspects is critical for transparency, interpretability, and future implementation of the results.
Identifiers
What 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.