Artificial Intelligence for Predicting Treatment Adherence in Opioid Use Disorder: A Scoping Review
Bibliographic record
Abstract
Abstract Background and Aim Opioid use disorder (OUD) is a chronic condition in which an individual engages in the persistent use of opioids that causes significant distress and negatively impacts their societal functioning. Treatment for OUD involves pharmacological therapies such as methadone, buprenorphine, and naltrexone, typically used in combination with behavioral interventions such as counselling and cognitive behavioural therapy. However, non-adherence to OUD treatment is high, potentially leading to negative outcomes like relapse and increased risk of overdose. Therefore, identifying patients at risk of treatment nonadherence is essential to ensure that OUD is adequately managed. Models utilizing AI and ML techniques have emerged as promising candidates to achieve risk stratification in this patient population. We conducted a scoping review to capture and systematically map existing literature on AI and ML applications predicting adherence to treatment in individuals with OUD. Methods Ovid MEDLINE, Embase, PsycINFO, Web of Science, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library were searched to identify and obtain peer-reviewed empirical research articles published from inception to October 7, 2024. Twenty-two studies were selected to be included in the review. Results All studies that matched our search criteria were published after 2018 and predominantly conducted in the United States. Random forest models were frequently identified as the top performer although significant variability in algorithms, evaluation metrics, and key predictors was noted in the literature. Conclusion The need for future research to cover more geographical locations, diversify patient populations, focus on a standardized group of models and outcomes, and utilize larger samples was highlighted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".