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 …

Abstract Background Machine Learning is increasingly used to predict rehabilitation
outcomes in stroke in the context of precision rehabilitation and patient-centered care …

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 …

Background Machine learning (ML) techniques are being increasingly adopted in the
medical field. Objective We developed a deep neural network (DNN) model and applied 2 …

Background Rehabilitation medicine is facing a new development phase thanks to a recent
wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This …

Objective This review aimed to summarize the use of machine learning for predicting the
potential benefits of stroke rehabilitation treatments, to evaluate the risk of bias of predictive …

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 …

The majority of rehabilitation research focuses on the comparative effectiveness of different
interventions in groups of patients, while much less is currently known regarding individual …

Background In the past years, robotic systems have become increasingly popular in upper
limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm …

Prior studies examining predictors of favorable clinical outcomes after upper limb robot-
assisted therapy (RT) have many shortcomings. Therefore, the aim of this study was to …