ArticleDrug and alcohol dependence2025
Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning.
Article in Drug and alcohol dependence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Wearable-derived measures of stress physiology in patients with opioid use disorder treated with methadone: A stress-task study in a real-world clinic with healthy controls.Drug and alcohol dependence reports · 2026Article
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4 authors.
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Abstract
introductionMachine learning may enable detection of opioid misuse to prevent opioid-related risks including overdose and opioid use disorder.
methodsHere, we collected 9238 datapoints from on-body sensors and cognitive tasks in a sample of 169 patients who were prescribed opioid analgesics to manage chronic pain. We categorized patients into one of two groups using the Current Opioid Misuse Measure (COMM): those showing signs of opioid misuse (MISUSE+, n = 116) and those without signs of opioid misuse (MISUSE-, n = 53). Heart rate variability and respiration rate were assessed while participants completed a Dot Probe task involving shifting attention towards and away from opioid-related and emotional cues, and a Go/No-Go task involving inhibition of automatic responses. Cross-sectional data (e.g., physiological responses, task reaction times, task accuracy) were analyzed with a temporal fusion transformer machine learning (ML) model to predict COMM opioid misuse status. We employed Leave-One-Group-Out (LOGO) cross-validation with the participants divided into 10 groups. Each cycle, one group was held out for testing, ensuring robust, unbiased model validation across different subsets of participants.
resultsThe ML model showed good predictive performance for identifying opioid misuse (AUC, 0.81; specificity, 0.78; sensitivity, 0.78). Behavioral responses were stronger predictors of misuse status than physiological signals.
conclusionsML models using data from cognitive tasks and on-body sensors detected opioid misuse with an accuracy comparable to gold-standard self-reported opioid misuse assessments. Wearable sensors may provide only incremental predictive power over behavioral responses. Our ML model should be benchmarked against objective measures of opioid misuse.
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