使用Fitbit生物信号监测物质使用:关于使用生态瞬间评估和被动传感来训练深度学习模型的案例研究
Shizhe Li1, Chunzhi Fan2, Ali Kargarandehkordi3
1Department of Statistics, Stanford University, Stanford, CA 94305, USA.
概括
使用Fitbit数据的个性化机器学习显示出检测物质使用的前景. 自主监督学习 (SSL) 模型改善了个性化的特征提取,增强了数字干预的早期检测能力.
科学领域:
- 数字健康数字健康
- 机器学习是机器学习.
- 可穿戴生物传感器
背景情况:
- 药物使用障碍影响数百万人,需要创新的检测方法.
- 可穿戴生物传感器数据为实时物质使用监测提供了潜力.
- 机器学习模型中的数据异质性阻碍了准确的物质使用检测.
研究的目的:
- 评估个性化的机器学习模型,通过可穿戴生物信号检测药物使用情况.
- 将传统的监督学习与自我监督学习 (SSL) 增强模型进行比较.
- 评估使用Fitbit数据用于物质使用检测的可行性.
主要方法:
- 收集了9名参与者的Fitbit Charge 5数据和生态瞬间评估.
- 实施了一个基准的1D-CNN监督学习模型.
- 开发了一种实验性SSL增强的CNN模型,用于改进个性化的特征提取.
主要成果:
- 与监督CNN (0.695) 相比,SSL增强型号在接收器操作特征曲线 (0.729) 下的平均面积更高.
- 最佳值选择允许平衡灵敏度和特异性.
- 这些发现表明Fitbit数据对于物质使用监测的潜力.
结论:
- 个性化机器学习,特别是SSL,显示出从可穿戴数据中检测物质使用的潜力.
- 需要进一步的大规模研究来验证这些发现在不同的人群中.
- 这种方法可以为实时数字干预物质使用障碍的发展提供信息.
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