Master Program in Long-Term Care, College of Nursing, Taipei Medical University, Taipei, Taiwan.
Department of Occupational Therapy, School of Health Professions, University of Texas Medical Branch, Galveston, TX.
Arch Phys Med Rehabil. 2022 Aug;103(8):1574-1581. doi: 10.1016/j.apmr.2021.12.006. Epub 2021 Dec 31.
This study aimed to develop and validate a machine learning-based short measure to assess 5 functions (the ML-5F) (activities of daily living [ADL], balance, upper extremity [UE] and lower extremity [LE] motor function, and mobility) in patients with stroke.
Secondary data from a previous study. A follow-up study assessed patients with stroke using the Barthel Index (BI), Postural Assessment Scale for Stroke (PASS), and Stroke Rehabilitation Assessment of Movement (STREAM) at hospital admission and discharge.
A rehabilitation unit in a medical center.
Patients (N=307) with stroke.
Not applicable.
The BI, PASS, and STREAM.
A machine learning algorithm, Extreme Gradient Boosting, was used to select 15 items from the BI, PASS, and STREAM, and transformed the raw scores of the selected items into the scores of the ML-5F. The ML-5F demonstrated good concurrent validity (Pearson's r, 0.88-0.98) and responsiveness (standardized response mean, 0.28-1.01).
The ML-5F comprises only 15 items but demonstrates sufficient concurrent validity and responsiveness to assess ADL, balance, UE and LE functions, and mobility in patients with stroke. The ML-5F shows great potential as an efficient outcome measure in clinical settings.
本研究旨在开发和验证一种基于机器学习的简短测量工具，以评估 5 项功能（ML-5F）（日常生活活动[ADL]、平衡、上肢[UE]和下肢[LE]运动功能以及移动能力），适用于脑卒中患者。
来自先前研究的二次数据。一项随访研究在患者入院和出院时使用巴氏指数（BI）、脑卒中姿势评估量表（PASS）和脑卒中运动康复评估量表（STREAM）评估脑卒中患者。
医疗中心的康复病房。
脑卒中患者（N=307）。
无。
BI、PASS 和 STREAM。
使用极端梯度提升机器学习算法从 BI、PASS 和 STREAM 中选择 15 项，将所选项目的原始得分转换为 ML-5F 的得分。ML-5F 表现出良好的同时效度（Pearson r，0.88-0.98）和反应度（标准化反应均值，0.28-1.01）。
ML-5F 仅包含 15 项，但足以评估脑卒中患者的 ADL、平衡、UE 和 LE 功能以及移动能力，具有足够的同时效度和反应度。ML-5F 在临床环境中作为一种高效的结局测量工具具有巨大潜力。
