Drug and alcohol dependence · 2025
Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning.
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Gullapalli Bhanu, Luo Yunfei, Rahman Tauhidur, Garland Eric L
Аннотация
INTRODUCTION: Machine learning may enable detection of opioid misuse to prevent opioid-related risks including overdose and opioid use disorder.
METHODS: Here, 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.
RESULTS: The 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.
CONCLUSIONS: ML 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.
METHODS: Here, 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.
RESULTS: The 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.
CONCLUSIONS: ML 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.