Practitioners in pediatric feeding programs often rely on single-case experimental designs and visual inspection to make treatment decisions (e.g., whether to change or keep a treatment in place). However, researchers have shown that this practice remains subjective, and there is no consensus yet on the best approach to support visual inspection results. To address this issue, we present the first application of a pediatric feeding treatment evaluation using machine learning to analyze treatment effects. A 5-year-old male with autism spectrum disorder participated in a 2-week home-based, behavior-analytic treatment program. We compared interrater agreement between machine learning and expert visual analysts on the effects of a pediatric feeding treatment within a modified reversal design. Both the visual analyst and the machine learning model generally agreed about the effectiveness of the treatment while overall agreement remained high. Overall, the results suggest that machine learning may provide additional support for the analysis of single-case experimental designs implemented in pediatric feeding treatment evaluations.

儿科喂养计划的从业者通常依靠单例实验设计和目视检查来做出治疗决定（例如，是否改变或保持治疗）。然而，研究人员表明，这种做法仍然是主观的，对于支持目视检查结果的最佳方法尚未达成共识。为了解决这个问题，我们提出了使用机器学习来分析治疗效果的儿科喂养治疗评估的第一个应用。一名患有自闭症谱系障碍的 5 岁男性参加了为期 2 周的以家庭为基础的行为分析治疗计划。我们比较了机器学习和专家视觉分析师在改进的逆转设计中对儿科喂养治疗的影响之间的一致性。视觉分析师和机器学习模型都普遍同意治疗的有效性，而总体一致性仍然很高。总体而言，结果表明机器学习可以为分析在儿科喂养治疗评估中实施的单例实验设计提供额外的支持。