ArticleCanadian journal of psychiatry. Revue canadienne de psychiatrie2023
Individualized Prospective Prediction of Opioid Use Disorder.
Article in Canadian journal of psychiatry. Revue canadienne de psychiatrie, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.Journal of addiction medicinePooled it
- Maturity Framework for Operationalizing Machine Learning Applications in Health Care: Scoping Review.Journal of medical Internet research · 2025Article
- Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning.Drug and alcohol dependence · 2025Article
- AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations.Journal of medical Internet research · 2025Review
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- A systematic review of machine learning applications in predicting opioid associated adverse events.NPJ digital medicine · 2025Article
- Early identification of children with Attention-Deficit/Hyperactivity Disorder (ADHD).PLOS digital health · 2024Article
- Predictive Model for Opioid Use Disorder in Chronic Pain: A Development and Validation Study.Biomedicines · 2024Article
- Prevalence and determinants of opioid use disorder among long-term opiate users in Golestan Cohort Study.BMC psychiatry · 2023Article
- Using machine learning to study the effect of medication adherence in Opioid Use Disorder.PloS one · 2022Article
- Effect of Diamorphine on Spatial Learning and Memory and Mitochondrial Function of Male Rat Brain.Iranian journal of pharmaceutical research : IJPRArticle
- 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
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOpioid use disorder (OUD) is a chronic relapsing disorder with a problematic pattern of opioid use, affecting nearly 27 million people worldwide. Machine learning (ML)-based prediction of OUD may lead to early detection and intervention. However, most ML prediction studies were not based on representative data sources and prospective validations, limiting their potential to predict future new cases. In the current study, we aimed to develop and
methodWe present an ensemble machine-learning model trained on a cross-linked Canadian administrative health data set from 2014 to 2018 (
resultsWith 6409 OUD cases in 2019 (mean [SD], 45.34 [14.28], 3400 males), our model prospectively predicted OUD cases at a high accuracy (balanced accuracy, 86%, sensitivity, 93%; specificity 79%). In accord with prior findings, the top risk factors for OUD in this model were opioid use indicators and a history of other substance use disorders.
conclusionOur study presents an individualized prospective prediction of OUD cases by applying ML to large administrative health datasets. Such prospective predictions based on ML would be essential for potential future clinical applications in the early detection of OUD.
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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.