针对夏威夷物质使用的个性化深度学习:被动感应和生态瞬间评估研究的协议
Yinan Sun1, Ali Kargarandehkordi1, Christopher Slade1
1Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
JMIR research protocols
|February 7, 2024
概括
这项研究探讨了使用人工智能 (AI) 和可穿戴传感器来预测夏威夷原住民,菲律宾人和太平洋岛民社区的甲基胺渴望. 个性化人工智能模型旨在改善药物滥用治疗的可访问数字治疗方法.
科学领域:
- 数字治疗学数字治疗学
- 医疗保健中的人工智能
- 可穿戴式生物感知器
背景情况:
- 人工智能驱动的数字疗法为服务不足的社区提供了可访问的解决方案,但夏威夷原住民,菲律宾人和太平洋岛民仍然在数字疗法和人工智能健康传感方面缺乏研究.
- 这些社区使用技术的速度与其他种族群体相比较,突出显示了定制数字健康干预措施的差距.
- 解决医疗保健差异需要了解这些特定人群中药物滥用远程监测的可行性.
研究的目的:
- 评估持续远程数字监测和生态瞬间评估在夏威夷原住民,菲律宾人和太平洋岛民社区的可行性.
- 使用纵向可穿戴传感器数据开发个性化的AI模型,用于实时预测甲基胺渴望事件.
- 为了整理一个新的Fitbit生物信号数据集,与自我报告的渴望和物质使用相关联.
主要方法:
- 开发个性化的人工智能和机器学习模型,用于对来自不同种族背景的40名个人进行甲基胺使用和渴望预测.
- 一个新的数据集的策划,将实时的Fitbit生物传感器读数与参与者报告的甲基胺使用和渴望相结合.
- 应用自主监督学习人工智能方法,培训神经网络没有明确的标签来预测生物传感器数据的渴望事件.
主要成果:
- 超过40人表达了兴趣,成功招募了最初的5名参与者,证明了可行性.
- 在招聘阶段遇到的物流挑战最小.
- 该研究正在积极收集数据,并将讨论遇到的后勤挑战及其影响.
结论:
- 个性化人工智能模型预计将通过适应个人参与者数据,优于传统的监督方法.
- 这种方法使得人工智能解决方案能够有效地使用有限的数据,并且本质上是公正的,对代表性不足的人群至关重要.
- 开发的模型旨在支持未来的人工智能驱动的药物滥用数字治疗方法,提高可访问性和减少差距.
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