Opioid misuse detection from cognitive and physiological data with temporal fusion deep learning

IF 3.6 2区 医学 Q1 PSYCHIATRY
Bhanu Gullapalli , Yunfei Luo , Tauhidur Rahman , Eric L. Garland
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引用次数: 0

Abstract

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.
基于认知和生理数据的阿片类药物滥用检测
机器学习可以检测阿片类药物滥用,以防止阿片类药物相关风险,包括过量和阿片类药物使用障碍。在这里,我们收集了169名服用阿片类镇痛药治疗慢性疼痛的患者的9238个数据点,这些数据点来自身体传感器和认知任务。我们使用当前阿片类药物滥用测量(COMM)将患者分为两组:显示阿片类药物滥用迹象的患者(滥用+,n = 116)和没有阿片类药物滥用迹象的患者(滥用-,n = 53)。当参与者完成一项Dot Probe任务(涉及将注意力转移到阿片类药物相关和情绪线索上)和一项Go/No-Go任务(涉及抑制自动反应)时,对心率变异性和呼吸率进行了评估。使用时间融合变压器机器学习(ML)模型分析横截面数据(例如生理反应,任务反应时间,任务准确性)以预测COMM阿片类药物滥用状态。我们采用Leave-One-Group-Out (LOGO)交叉验证,参与者分为10组。每个周期,一个组被进行测试,以确保在不同的参与者子集中进行稳健、无偏的模型验证。结果ML模型对阿片类药物滥用有较好的预测效果(AUC, 0.81;特异性,0.78;敏感性,0.78)。行为反应比生理信号更能预测滥用状况。结论使用认知任务数据和体表传感器的sml模型检测阿片类药物滥用的准确性与金标准自我报告的阿片类药物滥用评估相当。可穿戴式传感器可能只能对行为反应提供增量预测能力。我们的ML模型应该以阿片类药物滥用的客观测量为基准。
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来源期刊
Drug and alcohol dependence
Drug and alcohol dependence 医学-精神病学
CiteScore
7.40
自引率
7.10%
发文量
409
审稿时长
41 days
期刊介绍: Drug and Alcohol Dependence is an international journal devoted to publishing original research, scholarly reviews, commentaries, and policy analyses in the area of drug, alcohol and tobacco use and dependence. Articles range from studies of the chemistry of substances of abuse, their actions at molecular and cellular sites, in vitro and in vivo investigations of their biochemical, pharmacological and behavioural actions, laboratory-based and clinical research in humans, substance abuse treatment and prevention research, and studies employing methods from epidemiology, sociology, and economics.
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