Department of Gerontological Health Care, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.
Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Taoyuan, Taiwan.
Sci Rep. 2022 Jul 4;12(1):11235. doi: 10.1038/s41598-022-14986-1.
Health related quality of life (HRQOL) reflects individuals perceived of wellness in health domains and is often deteriorated after stroke. Precise prediction of HRQOL changes after rehabilitation interventions is critical for optimizing stroke rehabilitation efficiency and efficacy. Machine learning (ML) has become a promising outcome prediction approach because of its high accuracy and easiness to use. Incorporating ML models into rehabilitation practice may facilitate efficient and accurate clinical decision making. Therefore, this study aimed to determine if ML algorithms could accurately predict clinically significant HRQOL improvements after stroke sensorimotor rehabilitation interventions and identify important predictors. Five ML algorithms including the random forest (RF), k-nearest neighbors (KNN), artificial neural network, support vector machine and logistic regression were used. Datasets from 132 people with chronic stroke were included. The Stroke Impact Scale was used for assessing multi-dimensional and global self-perceived HRQOL. Potential predictors included personal characteristics and baseline cognitive/motor/sensory/functional/HRQOL attributes. Data were divided into training and test sets. Tenfold cross-validation procedure with the training data set was used for developing models. The test set was used for determining model performance. Results revealed that RF was effective at predicting multidimensional HRQOL (accuracy: 85%; area under the receiver operating characteristic curve, AUC-ROC: 0.86) and global perceived recovery (accuracy: 80%; AUC-ROC: 0.75), and KNN was effective at predicting global perceived recovery (accuracy: 82.5%; AUC-ROC: 0.76). Age/gender, baseline HRQOL, wrist/hand muscle function, arm movement efficiency and sensory function were identified as crucial predictors. Our study indicated that RF and KNN outperformed the other three models on predicting HRQOL recovery after sensorimotor rehabilitation in stroke patients and could be considered for future clinical application.
健康相关生活质量（HRQOL）反映了个体在健康领域的自我感知健康状况，并且经常在中风后恶化。准确预测康复干预后 HRQOL 的变化对于优化中风康复效率和效果至关重要。机器学习（ML）因其准确性高且易于使用而成为一种很有前途的结果预测方法。将 ML 模型纳入康复实践中可能有助于进行高效和准确的临床决策。因此，本研究旨在确定 ML 算法是否可以准确预测中风感觉运动康复干预后临床上显著的 HRQOL 改善，并确定重要的预测因素。使用了包括随机森林（RF）、k-最近邻（KNN）、人工神经网络、支持向量机和逻辑回归在内的五种 ML 算法。纳入了 132 名慢性中风患者的数据。使用中风影响量表评估多维和整体自我感知的 HRQOL。潜在的预测因素包括个人特征和基线认知/运动/感觉/功能/HRQOL 属性。数据分为训练集和测试集。使用训练数据集的十折交叉验证程序来开发模型。使用测试集确定模型性能。结果表明，RF 能够有效预测多维 HRQOL（准确性：85%；接受者操作特征曲线下的面积，AUC-ROC：0.86）和整体感知恢复（准确性：80%；AUC-ROC：0.75），KNN 能够有效预测整体感知恢复（准确性：82.5%；AUC-ROC：0.76）。年龄/性别、基线 HRQOL、手腕/手部肌肉功能、手臂运动效率和感觉功能被确定为关键预测因素。我们的研究表明，RF 和 KNN 在预测中风患者感觉运动康复后 HRQOL 恢复方面优于其他三种模型，可考虑用于未来的临床应用。
