用药理动力学告知的神经网络来预测使用可穿戴传感器的阿片类药物给药时刻
Bhanu Teja Gullapalli1, Stephanie Carreiro2, Brittany P Chapman2
1University of California San Diego.
概括
可穿戴的手腕传感器可以使用生理信号来检测口服阿片类药物管理. 基于药理动力学的模型提高了预测阿片类药物时间的可靠性,提供了客观的日常阿片类药物使用检测.
科学领域:
- 生物医学工程 生物医学工程
- 药理学 药理学是指药理学的学科.
- 数字健康数字健康
背景情况:
- 长期使用处方阿片类药物会增加滥用,阿片类药物使用障碍 (OUD) 和过量服用的风险.
- 目前的监测方法,如自我报告和毒理学测试,在准确性和可行性方面存在局限性.
- 尽管可穿戴技术在健康指标中的应用日益增长,但其监测阿片类药物使用的潜力基本上尚未被探索.
研究的目的:
- 调查使用手腕佩戴传感器检测口服阿片类药物管理的可行性.
- 评估药理动力学信息模型对检测阿片类药物使用的准确性的影响.
- 探索客观的,数据驱动的方法,在现实环境中监测阿片类药物使用.
主要方法:
- 从手腕传感器收集了42名参与者处方阿片类药物的生理数据.
- 通过电子医疗记录和自我报告跟踪阿片类药物管理.
- 开发并应用了一种自我监督的预训练模型,将阿片类药代动力学纳入传感器数据.
主要成果:
- 通过手腕传感器的生理信号成功检测到口服阿片类药物管理.
- 证明了基于药理动力学的模型显著提高了预测阿片类药物服药时间的可靠性.
- 在住院和门诊环境中实现了阿片类药物管理的可靠检测.
结论:
- 手腕佩戴的传感器与药理动力学知情模型相结合,可以客观地检测阿片类药物管理.
- 这种方法有望改善对处方阿片类药物使用的监测,并减轻相关风险.
- 数据驱动的可穿戴技术为现实世界阿片类药物使用监控提供了一条新的途径.
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