Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■尽管临床机器学习（ML）算法在预测最佳卒中康复结果方面提供了有希望的潜力，在接受此类干预的偏瘫性卒中患者中,他们确定有利结局和识别机器人辅助步态训练(RAGT)应答者的特定能力仍未被探索.
■我们旨在根据功能性损害领域特征的国际分类（Fugl-Meyer评估（FMA），改良的Barthel指数相关步态量表(MBI)，Berg平衡量表（BBS）），并显示亚急性中风患者对机器人辅助步态训练（RAGT）的反应性。
■获得并分析了187名接受12周WalkbotRAGT干预的亚急性卒中患者的数据。总的来说,18个潜在的预测因子包括人口统计学特征以及功能和结构特征的基线得分。五种预测机器学习模型，包括决策树，随机森林,极限梯度提升，轻型梯度增压机，和明确的提升，被使用。
■初始和最终的论坛，初始BBS,最终修改的Ashworth量表，初始MBI分数是重要的特征，预测功能改善。在预测亚急性卒中患者RAGT后的功能恢复方面，极限梯度增强与其他模型相比表现优异。
■极限梯度提升可能是一种宝贵的预后工具，为临床医生和护理人员提供了一个强大的框架，以就最佳应答者的识别做出精确的临床决策，并有效地确定那些最有可能从RAGT干预措施中获得最大益处的人。
■我们旨在根据功能性损害领域特征的国际分类（Fugl-Meyer评估（FMA），改良的Barthel指数相关步态量表(MBI)，Berg平衡量表（BBS）），并显示亚急性中风患者对机器人辅助步态训练（RAGT）的反应性。
■获得并分析了187名接受12周WalkbotRAGT干预的亚急性卒中患者的数据。总的来说,18个潜在的预测因子包括人口统计学特征以及功能和结构特征的基线得分。五种预测机器学习模型，包括决策树，随机森林,极限梯度提升，轻型梯度增压机，和明确的提升，被使用。
■初始和最终的论坛，初始BBS,最终修改的Ashworth量表，初始MBI分数是重要的特征，预测功能改善。在预测亚急性卒中患者RAGT后的功能恢复方面，极限梯度增强与其他模型相比表现优异。
■极限梯度提升可能是一种宝贵的预后工具，为临床医生和护理人员提供了一个强大的框架，以就最佳应答者的识别做出精确的临床决策，并有效地确定那些最有可能从RAGT干预措施中获得最大益处的人。
