Computational Interpersonal Communication Model for Screening Autistic Toddlers: A Case Study of Response-to-Name

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 6; pp. 3683 - 3694
• Main Authors: Nie, Wei, Zhou, Bingrui, Wang, Zhiyong, Chen, Bowen, Wang, Xinming, Hu, Chunchun, Li, Huiping, Xu, Qiong, Xu, Xiu, Liu, Honghai
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autism; Autism screening; Autism Spectrum Disorder; Child, Preschool; Communication; Computational interpersonal communication; Computational modeling; Computer applications; Decision making; Diagnosis, Computer-Assisted - methods; Female; Humans; Indicators; Infant; Interpersonal communication; Interpersonal Relations; Machine Learning; Male; Markov chain; Pediatrics; Personal communication; Ratings; Receivers; Response to name; Sensors; Sociability measurement; Statistical analysis; Stochastic processes; Stochasticity; Testing; Variable speed drives
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3388836

Interpersonal communication facilitates symptom measures of autistic sociability to enhance clinical decision-making in identifying children with autism spectrum disorder (ASD). Traditional methods are carried out by clinical practitioners with assessment scales, which are subjective to quantify. Recent studies employ engineering technologies to analyze children's behaviors with quantitative indicators, but these methods only generate specific rule-driven indicators that are not adaptable to diverse interaction scenarios. To tackle this issue, we propose a Computational Interpersonal Communication Model (CICM) based on psychological theory to represent dyadic interpersonal communication as a stochastic process, providing a scenario-independent theoretical framework for evaluating autistic sociability. We apply CICM to the response-to-name (RTN) with 48 subjects, including 30 toddlers with ASD and 18 typically developing (TD), and design a joint state transition matrix as quantitative indicators. Paired with machine learning, our proposed CICM-driven indicators achieve consistencies of 98.44% and 83.33% with RTN expert ratings and ASD diagnosis, respectively. Beyond outstanding screening results, we also reveal the interpretability between CICM-driven indicators and expert ratings based on statistical analysis.