TY - JOUR AU - Thakkar, Hiren Kumar AU - Liao, Wan-wen AU - Wu, Ching-yi AU - Hsieh, Yu-Wei AU - Lee, Tsong-Hai PY - 2020 DA - 2020/09/29 TI - Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches JO - Journal of NeuroEngineering and Rehabilitation SP - 131 VL - 17 IS - 1 AB - 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 accuracy 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 contemporary task-oriented intervention in chronic stroke patients and identify important predictors for building machine learning prediction models. SN - 1743-0003 UR - https://doi.org/10.1186/s12984-020-00758-3 DO - 10.1186/s12984-020-00758-3 ID - Thakkar2020 ER -