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通过适应机器学习方法来个性化数字疼痛管理

Yifat Fundoiano-Hershcovitz¹, Keren Pollak², Pavel Goldstein²

¹Dario Health, Caesarea, Israel.

|September 21, 2023

概括

这项研究引入了使用数字治疗 (DTx) 进行个性化疼痛管理的新框架. 该模型通过分析用户数据并提高可解释性以获得更好的患者结果来提高治疗疗效.

科学领域:

数字健康数字健康
计算医学是一种计算医学.
生物统计学生物统计学

背景情况:

数字疗法 (DTx) 在治疗疼痛方面表现出不同的疗效.
机器学习 (ML) 提供个性化,但往往缺乏临床解释性.
经典的ML模型与纵向DTx数据和非线性模式作斗争.

研究的目的:

提出一个个性化疼痛管理的分析框架,使用零碎混合效应模型树.
解决数据依赖性,非线性轨迹,并提高DTx.中的模型解释性.
通过考虑个人用户特征来个性化疼痛管理.

主要方法:

实现了一个逐步混合效果模型树框架.
分析了来自3610名用户的8周姿势生物反训练数据.
开发了针对疼痛,姿势质量和训练持续时间的个性化模型,包括年龄,性别和BMI.

主要成果:

在前3周内,疼痛和姿势质量显著改善,随后持续效应.
年龄调节的疼痛波动;年龄和性别交互调节的姿势质量轨迹.
训练时间最初在老年用户中增加,然后随着时间的推移在所有用户中减少.

结论:

该框架允许个性化的DTx疗效研究用于疼痛管理.
它考虑了用户的特点,提高了可解释性.
未来的研究可以从结合额外的用户特征中受益.

关键词:

背部疼痛背部疼痛决策树决策树是一个决策树.数字治疗学数字治疗学机器学习是机器学习.混合模型的混合模型.个性化的数字治疗方法姿势生物反反姿势生物反

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