从时间融合深度学习的认知和生理数据中检测阿片类药物滥用.
Bhanu Gullapalli1, Yunfei Luo1, Tauhidur Rahman1
1Halıcıoğlu Data Science Institute, Department of Computer Science and Engineering, University of California San Diego, United States.
Drug and alcohol dependence
|July 20, 2025
概括
机器学习模型使用认知任务和可穿戴传感器的数据准确地检测阿片类药物滥用. 行为反应比生理信号更有预测力,用于识别滥用.
科学领域:
- 数字健康数字健康
- 机器学习应用程序 机器学习应用程序
- 临床信息学 临床信息学
背景情况:
- 滥用阿片类药物存在重大风险,包括过量服用和阿片类药物使用障碍.
- 早期发现阿片类药物滥用对于预防不良结果至关重要.
- 机器学习 (ML) 提供了识别有风险的个人的潜力.
研究的目的:
- 开发和验证用于检测阿片类药物滥用的ML模型.
- 评估认知任务执行和生理数据的预测价值.
- 将基于ML的检测与传统的评估方法进行比较.
主要方法:
- 收集了来自169名处方阿片类止痛药的患者的数据,包括体内传感器数据和认知任务的表现 (Dot Probe,Go/No-Go).
- 利用时间融合变压器ML模型,根据当前阿片类药物滥用措施 (COMM) 分类来预测阿片类药物滥用状态.
- 员工休假一组退出 (LOGO) 交叉验证,以进行强大而公正的模型绩效评估.
主要成果:
- 该ML模型在阿片类药物滥用检测方面取得了良好的预测性能 (AUC,0.81).
- 在认知任务期间的行为反应显示出比生理信号 (心率变化,呼吸率) 更强大的预测能力.
- 两个指标的模型灵敏度和特异性为0.78.
结论:
- 整合认知任务数据和传感器数据的ML模型可以检测阿片类药物滥用,其准确性与自我报告措施相美.
- 穿戴式传感器数据可能提供超出行为反应的有限的额外预测价值.
- 进一步的研究应该将ML模型与阿片类药物滥用的客观措施进行比较.
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