學院	醫學院
系所	職能治療學系
題名	Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: A machine learning study.
作者	Chen, Y-W., Lin, K-C.*, Li, Y-C., & Lin, C-J.
期刊名稱	Journal of NeuroEngineering and Rehabilitation
發表日期	2023
著作性質	原著
語文	英文
關鍵字	Stroke rehabilitation, Prognosis, Prediction, Patient‑reported outcome measures, Activities of daily living, Machine learning
摘要	Background: Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient‑centered care. However, predictors for patient‑centered outcome measures for activities and participation in stroke rehabilitation requires further investigation. Methods: This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient‑reported outcome measures of daily activities (i.e, the Motor Activity Log and the Nottingham Extended Activities of Daily Living) and participation (i.e, the Activities of Daily Living domain of the Stroke Impact Scale). Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k‑nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection. Results: Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper‑limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best‑performing models with 18 features ranged between 0.72 and 0.96. Those of the best‑performing models with fewer features ranged between 0.72 and 0.84. Conclusions: Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient‑reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient‑centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation.