Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machin
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RESEARCH
Predicting clinically significant motor function improvement after contemporary task‑oriented interventions using machine learning approaches Hiren Kumar Thakkar1†, Wan‑wen Liao2† , Ching‑yi Wu2,3,4* , Yu‑Wei Hsieh2,3,4* and Tsong‑Hai Lee5,6
Abstract Background: Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accu‑ racy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabilitation outcomes in chronic stroke patients for common contemporary task-oriented interventions remains largely unexplored. This study aimed to determine the accuracy and performance of machine learning to predict clinically significant motor function improvements after contem‑ porary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models. Methods: This study was a secondary analysis of data using two common machine learning approaches, which were the k-nearest neighbor (KNN) and artificial neural network (ANN). Chronic stroke patients (N = 239) that received 30 h of task-oriented training including the constraint-induced movement therapy, bilateral arm training, robot-assisted therapy and mirror therapy were included. The Fugl-Meyer assessment scale (FMA) was the main outcome. Potential predictors include age, gender, side of lesion, time since stroke, baseline functional status, motor function and quality of life. We divided the data set into a training set and a test set and used the cross-validation procedure to construct machine learning models based on the training set. After the models were built, we used the test data set to evaluate the accuracy and prediction performance of the models. Results: Three important predictors were identified, which were time since stroke, baseline functional independence measure (FIM) and baseline FMA scores. Models for predicting motor function improvements were accurate. The pre‑ diction accuracy of the KNN model was 85.42% and area under the receiver operating characteristic curve (AUC-ROC) was 0.89. The prediction accuracy of the ANN model was 81.25% and the AUC-ROC was 0.77. Conclusions: Incorporating machine learning into clinical outcome prediction using three key predictors includ‑ ing time since stroke, baseline functional and motor ability may help clinicians/therapists to identify patients that are
*Correspondence: [email protected]; [email protected] † Hiren Kumar Thakkar and Wan-wen Liao contributed equally to this work 2 Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Taoyuan, Taiwan Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed und
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