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首页> 外文期刊>JMIR Medical Informatics >Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study
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Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study

机译：无监督机器学习，用于识别挑战行为型材，以探索自闭症谱系紊乱儿童的基于群体的治疗疗效：回顾性数据分析研究

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Background Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking. Objective The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups. Methods Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment hours, cluster assignment, and gender. Results Seven clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by low levels of stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression ( P .003). Conclusions These findings have implications on the treatment of individuals with autism spectrum disorder. Self-injurious behavior and aggression were prevalent among participants with the worst treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify types of autism spectrum disorder shows promise.
机译：背景技术挑战性行为在具有自闭症谱系障碍的个人中普遍存在;然而，缺乏研究探索具有挑战性行为对治疗反应的影响。目的本研究的目的是根据不同挑战行为的参与识别自闭症谱系障碍的类型，并评估组之间治疗响应的差异。方法分析了关于854例自闭症谱系疾病的挑战性行为和治疗进展的回顾性数据。基于8个观察到的具有挑战性行为的参与者进行聚类，并进行多元线性回归，以测试技能掌握和治疗时间，群集分配和性别之间的相互作用。结果确定了七种群集，这表明了一个占主导地位挑战性的行为。对于一些簇，发现了治疗反应的显着差异。具体地，发现特征在于低水平的刻板印象的簇具有比特征的簇显着更高水平的技能掌握，其特征是自我伤害行为和侵略（P <.003）。结论这些调查结果对自闭症谱系障碍的治疗有影响。在最糟糕的治疗反应的参与者中，自我伤害的行为和侵略是普遍存在的，因此旨在解决这些挑战性行为的干预可能值得优先考虑。此外，使用无监督的机器学习模型来识别自闭症谱系障碍的类型显示承诺。

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