Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, USA.
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
J Neuroeng Rehabil. 2023 Feb 21;20(1):24. doi: 10.1186/s12984-023-01148-1.
Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone's subsequent UL performance category.
To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories.
This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance.
A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26-30% better classification) but had only modest cross-validation accuracy (48-55% out of bag classification).
UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA.
加速度计可直接测量上肢（UL）活动。最近，已经形成了多维 UL 性能类别，以更全面地衡量日常生活中的 UL 使用情况。预测中风后的运动结果具有巨大的临床应用价值，下一步是探索哪些因素可能预测某人随后的 UL 表现类别。
探讨不同的机器学习技术如何用于了解中风后早期的临床测量和参与者人口统计学数据与随后的 UL 表现类别之间的关系。
本研究分析了先前队列的两个时间点的数据（n=54）。使用的数据是参与者特征和中风后早期的临床测量值，以及稍后中风后的 UL 表现的预先建立的类别。使用不同的机器学习技术（单个决策树、袋装树和随机森林）构建具有不同输入变量的预测模型。使用解释能力（样本内准确性）、预测能力（袋外误差估计）和变量重要性来量化模型性能。
总共构建了七个模型，包括一个单个决策树、三个袋装树和三个随机森林。UL 损伤和能力的测量值是预测随后 UL 表现类别的最重要指标，无论使用哪种机器学习算法。其他非运动临床测量值也成为关键预测指标，而参与者人口统计学预测指标（除年龄外）在大多数模型中都不太重要。使用袋装算法构建的模型在样本内准确性方面优于单个决策树（分类准确性提高 26-30%），但交叉验证准确性仅略有提高（袋外分类准确率为 48-55%）。
在这项探索性分析中，无论使用哪种机器学习算法，UL 临床测量值都是预测随后 UL 表现类别的最重要指标。有趣的是，当输入变量的数量增加时，认知和情感测量值也成为重要的预测指标。这些结果强化了这样一种观点，即 UL 的表现，在体内，不是身体功能或运动能力的简单产物，而是一个复杂的现象，取决于许多生理和心理因素。通过使用机器学习，这种探索性分析是预测 UL 表现的一个富有成效的步骤。试验注册号 NA。
