Seonmi Park, Jongeun Choi, Yonghoon Kim, Joshua Sung H You
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引用次数: 0
Abstract
Background: Although clinical machine learning (ML) algorithms offer promising potential in forecasting optimal stroke rehabilitation outcomes, their specific capacity to ascertain favorable outcomes and identify responders to robotic-assisted gait training (RAGT) in individuals with hemiparetic stroke undergoing such intervention remains unexplored.
Objective: We aimed to determine the best predictive model based on the international classification of functioning impairment domain features (Fugl- Meyer assessment (FMA), Modified Barthel index related-gait scale (MBI), Berg balance scale (BBS)) and reveal their responsiveness to robotic assisted gait training (RAGT) in patients with subacute stroke.
Methods: Data from 187 people with subacute stroke who underwent a 12-week Walkbot RAGT intervention were obtained and analyzed. Overall, 18 potential predictors encompassed demographic characteristics and the baseline score of functional and structural features. Five predictive ML models, including decision tree, random forest, eXtreme Gradient Boosting, light gradient boosting machine, and categorical boosting, were used.
Results: The initial and final BBS, initial BBS, final Modified Ashworth scale, and initial MBI scores were important features, predicting functional improvements. eXtreme Gradient Boosting demonstrated superior performance compared to other models in predicting functional recovery after RAGT in patients with subacute stroke.
Conclusion: eXtreme Gradient Boosting may be an invaluable prognostic tool, providing clinicians and caregivers with a robust framework to make precise clinical decisions regarding the identification of optimal responders and effectively pinpoint those who are most likely to derive maximum benefits from RAGT interventions.
期刊介绍:
NeuroRehabilitation, an international, interdisciplinary, peer-reviewed journal, publishes manuscripts focused on scientifically based, practical information relevant to all aspects of neurologic rehabilitation. We publish unsolicited papers detailing original work/research that covers the full life span and range of neurological disabilities including stroke, spinal cord injury, traumatic brain injury, neuromuscular disease and other neurological disorders.
We also publish thematically organized issues that focus on specific clinical disorders, types of therapy and age groups. Proposals for thematic issues and suggestions for issue editors are welcomed.