ArticleJournal of substance use and addiction treatment2026
Applying machine learning in predicting medication treatment outcomes for opioid use disorder.
Article in Journal of substance use and addiction treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Applying machine learning in predicting medication treatment outcomes for opioid use disorder.Journal of substance use and addiction treatment · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundWhile medication for opioid use disorder (MOUD) is effective for a significant proportion of patients, many return to using opioids during treatment. Understanding which factors lead to successful treatment informs the development of implementation approaches that can improve outcomes. This manuscript and its accompanying website provide an applied introduction to interpretable machine learning for clinical investigators interested in predicting treatment response for people using MOUD.
methodsThis study applied machine learning (ML) algorithms (K-Nearest Neighbors (KNN), logistic regression with and without regularization, Multivariate Additive Regression Splines (MARS), Support Vector Machines, Classification and Regression Trees (CART), Random Forest, Bayesian Additive Regression Trees (BART), Boosted Trees, Neural Networks) to predict failure of treatment in a collection of 2478 individuals who had participated in the three largest pragmatic, clinical trials of MOUD.
resultsAll models produced Receiver Operating Characteristic Area Under the Curve (ROC AUC) estimates in the range of 0.62 to 0.67 using cross-validation data and the optimal model, random forest, achieved 0.65 using testing data. The algorithms nearly universally identified predictive features such as age, intravenous drug use days, study medication, and study site. Most algorithms also identified various aspects of smoking. Only the algorithms that detect complex non-linear trends identified details from timeline follow-back. One algorithm, BART, performed well while devaluing all treatment-specific details.
conclusionAfter explaining how to apply, compare, and contrast various ML workflows, the results show that while overall modeling performance is similar across the models developed, the use of different algorithms identifies different sets of predictive features. Previous research has not recognized some features as important for predicting treatment outcomes. A companion website introduces clinical investigators to the concepts and implementations this study presents. That site also provides a detailed annotated blueprint to fully replicate, or even expand, this work.
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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.