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监督式机器学习:一种预测自闭症谱系障碍儿童运动干预后结果的新方法。

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder.

作者信息

Sun Zhiyuan, Yuan Yunhao, Dong Xiaoxiao, Liu Zhimei, Cai Kelong, Cheng Wei, Wu Jingjing, Qiao Zhiyuan, Chen Aiguo

机构信息

College of Physical Education, Yangzhou University, Yangzhou 225127, China.

Institute of Sports, Exercise and Brain, Yangzhou University, Yangzhou 225127, China.

出版信息

Int J Clin Health Psychol. 2023 Oct-Dec;23(4):100409. doi: 10.1016/j.ijchp.2023.100409. Epub 2023 Sep 7.

Abstract

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

摘要

自闭症谱系障碍(ASD)儿童之间的个体差异可能使得从特定的运动干预计划中获得可比的益处具有挑战性。一种预测ASD儿童运动干预可能结果并最大化其益处的新方法需要进一步探索。以迷你篮球训练计划(MBTP)研究改善ASD儿童的症状表现为例,我们使用监督机器学习方法,基于ASD儿童的个体差异预测可能的干预结果,并研究和验证了该方法的有效性。在一项长期研究中,我们纳入了41名接受MBTP的ASD儿童。在干预前,我们收集了他们的临床信息、行为因素和脑结构指标作为候选因素。为了执行回归和分类任务,我们选择了监督机器学习方法中的随机森林算法,并使用交叉验证方法来确定预测结果的可靠性。回归任务用于预测ASD儿童在MBTP后的社交沟通障碍结果,并使用可解释方差来评估预测性能。分类任务用于区分ASD儿童的核心症状结果组,并基于准确性评估预测性能。我们发现随机森林模型可以预测MBTP后社交沟通障碍的结果(平均可解释方差为30.58%)和核心症状(平均准确率为66.12%),证实了监督机器学习方法可以预测ASD儿童的运动干预结果。我们的研究结果提供了一种新颖且可靠的方法,用于提前识别最有可能从特定运动干预计划中受益的ASD儿童,并为建立ASD儿童个性化运动干预计划推荐系统奠定了坚实基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/236c/10498172/efb511f566b4/gr1.jpg

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