Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
Sci Rep. 2022 Feb 28;12(1):3255. doi: 10.1038/s41598-022-07299-w.
Data science advances in behavioral signal processing and machine learning hold the promise to automatically quantify clinically meaningful behaviors that can be applied to a large amount of data. The objective of this study was to identify an automated behavioral marker of treatment response in social communication in children with autism spectrum disorder (ASD). First, using an automated computational method, we successfully derived the amount of time it took for a child with ASD and an adult social partner (N pairs = 210) to respond to each other while they were engaged in conversation bits ("latency") using recordings of brief, natural social interactions. Then, we measured changes in latency at pre- and post-interventions. Children with ASD who were receiving interventions showed significantly larger reduction in latency compared to those who were not receiving interventions. There was also a significant group difference in the changes in latency for adult social partners. Results suggest that the automated measure of latency derived from natural social interactions is a scalable and objective method to quantify treatment response in children with ASD.
行为信号处理和机器学习方面的数据科学进展有望实现对具有临床意义的行为进行自动量化，而这些行为可以应用于大量数据。本研究的目的是确定自闭症谱系障碍（ASD）儿童社交沟通中治疗反应的自动行为标志物。首先，我们使用自动化计算方法，成功地计算出 ASD 儿童和成人社交伙伴（N 对= 210）在进行简短自然社交互动时相互回应的时间（“延迟”）。然后，我们测量了干预前后的延迟变化。接受干预的 ASD 儿童的延迟时间显著减少，而未接受干预的儿童则没有。成人社交伙伴的延迟变化也存在显著的组间差异。结果表明，从自然社交互动中得出的自动延迟测量方法是量化 ASD 儿童治疗反应的一种可扩展且客观的方法。
