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Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach
In clinical practice, therapists often rely on clinical outcome measures to quantify a patient ’s impairment and function. Predicting a patient’s discharge outcome using baseline clinical information may help c...
Source: Journal of NeuroEngineering and Rehabilitation - June 10, 2020 Category: Rehabilitation Authors: Yaar Harari, Megan K. O ’Brien, Richard L. Lieber and Arun Jayaraman Tags: Research Source Type: research

Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches
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 ac...
Source: Journal of NeuroEngineering and Rehabilitation - September 29, 2020 Category: Rehabilitation Authors: Hiren Kumar Thakkar, Wan-wen Liao, Ching-yi Wu, Yu-Wei Hsieh and Tsong-Hai Lee Tags: Research Source Type: research

Generalizing the predictive relationship between 1-month motor skill retention and Rey –Osterrieth Delayed Recall scores from nondemented older adults to individuals with chronic stroke: a short report
Motor learning is fundamental to motor rehabilitation outcomes. There is growing evidence from non-neurological populations supporting the role of visuospatial memory function in motor learning, but current pr...
Source: Journal of NeuroEngineering and Rehabilitation - June 3, 2021 Category: Rehabilitation Authors: Jennapher Lingo VanGilder, Andrew Hooyman, Pamela R. Bosch and Sydney Y. Schaefer Tags: Short report Source Type: research

Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders
Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct ...
Source: Journal of NeuroEngineering and Rehabilitation - December 18, 2021 Category: Rehabilitation Authors: Chao-Yang Kuo, Chia-Wei Liu, Chien-Hung Lai, Jiunn-Horng Kang, Sung-Hui Tseng and Emily Chia-Yu Su Tags: Research Source Type: research

Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review
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 phenomenon, combined with new trends ...
Source: Journal of NeuroEngineering and Rehabilitation - June 3, 2022 Category: Rehabilitation Authors: Silvia Campagnini, Chiara Arienti, Michele Patrini, Piergiuseppe Liuzzi, Andrea Mannini and Maria Chiara Carrozza Tags: Review Source Type: research

The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke
Robots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impa...
Source: Journal of NeuroEngineering and Rehabilitation - January 27, 2023 Category: Rehabilitation Authors: Delowar Hossain, Stephen H. Scott, Tyler Cluff and Sean P. Dukelow Tags: Research Source Type: research

Autonomy support encourages use of more-affected arm post-stroke
Autonomy support, which involves providing individuals the ability to control their own behavior, is associated with improved motor control and learning in various populations in clinical and non-clinical sett...
Source: Journal of NeuroEngineering and Rehabilitation - September 7, 2023 Category: Rehabilitation Authors: Sujin Kim, Yumi Shin, Yeonwoo Jeong, Seungyoung Na and Cheol E. Han Tags: Research Source Type: research

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