Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median R EN 2 = 0 . 91 , R RF 2 = 0 . 88 , R ANN 2 = 0 . 83 , R SVM 2 = 0 . 79 , R CART 2 = 0 . 70 ; P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.

中文翻译：

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机器学习方法预测慢性中风治疗后个体上肢运动障碍

背景。准确预测慢性卒中患者治疗后上肢运动功能的临床损伤对临床医生来说是一项艰巨的任务，但却是制定适当治疗策略的关键。机器学习是一种非常有前途的途径，可用于提高临床实践中的预测准确性。目标。目的是使用人口统计学、临床、神经生理学和影像学输入变量评估 5 种机器学习方法在预测慢性卒中患者干预后上肢运动障碍方面的性能。方法。共纳入 102 名患者（女性：31%，年龄 61 ± 11 岁）。上肢 Fugl-Meyer 评估 (UE-FMA) 用于评估干预前后上肢的运动障碍。弹性网（EN），支持向量机，人工神经网络、分类和回归树以及随机森林被用于预测干预后 UE-FMA。使用交叉验证的 R2 比较方法的性能。结果。在使用人口统计学和基线临床数据（中位数 R EN 2 = 0 . 91，R RF 2 = 0 . 88，R ANN 2 = 0 . 83，R SVM 2 = 0）预测干预后 UE-FMA 方面，EN 的表现明显优于其他方法. 79 , R CART 2 = 0 . 70 ; P < .05)。干预前 UE-FMA 和受影响和未受影响半球之间的运动阈值 (MT) 差异是最强的预测因素。MT 的差异比受影响半球中运动诱发电位 (MEP) 的缺失或存在更重要。结论。机器学习方法可以使临床医生准确预测慢性中风患者的干预后 UE-FMA。MT 的半球间差异是慢性卒中患者对治疗反应的重要预测指标，因此可以纳入前瞻性研究。