仪器定时和实时测试和基于机器学习的Levodopa反应评估:一个试点研究
Jing He1, Lingyu Wu2,3, Wei Du1
1Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, People's Republic of China.
Journal of neuroengineering and rehabilitation
|September 18, 2024
概括
使用可穿戴传感器和仪器定时启动和启动 (iTUG) 测试的新机器学习方法有效评估帕金森病患者的利沃多巴反应,为传统方法提供了更客观的替代方案.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 急性勒沃多巴挑战测试 (ALCT) 是评估帕金森病中勒沃多巴反应 (LR) 的标准.
- 目前的ALCT依赖于运动障碍学会的帕金森病统一评级表第三部分 (MDS-UPDRS III),这可能是主观和繁的.
研究的目的:
- 开发和验证一种机器学习 (ML) 方法,使用仪器定时升级和启动 (iTUG) 测试来客观评估LR.
- 为了将基于ML的iTUG方法与LR评估的传统ALCT进行比较.
主要方法:
- 42名帕金森症患者在OFF和ON药物状态下接受了ALCT和iTUG测试.
- 从iTUG传感器数据中提取了动力学,时间和频率域特征.
- 两种XGBoost模型 (LRR和MSE) 经过训练,使用离开一个主体的交叉验证来预测LR.
主要成果:
- 基于ML的勒沃多巴反应回归 (LRR) 模型显示与ALCT高度一致 (ICC = 0.95).
- 该LRR模型实现了0.94的积极预测值,用于检测积极的Levodopa反应.
结论:
- 应用于iTUG可穿戴传感器数据的ML提供了一种有效和全面的方法来评估勒沃多巴反应.
- 这种方法对预测多巴胺疗法在帕金森病中的疗效有很大希望.
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