2025/10/22 by Audrey Dong, Dong, Audrey, Claire Xu +7
Neuroscience · Psychology · #Applications (stat.AP) #Autism Spectrum Disorder Research #Digital Mental Health Interventions #FOS: Computer and information sciences #Family and Disability Support Research #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2510.26808
openalex publication_date 2025/10/22 · openalex created_date 2025/11/05 · openalex updated_date 2026/07/28
Caregivers of individuals with autism spectrum disorder (ASD) often find the 77-item Autism Treatment Evaluation Checklist (ATEC) burdensome, limiting its use for routine monitoring. This study introduces a generalizable machine learning framework that seeks to shorten assessments while maintaining evaluative accuracy. Using longitudinal ATEC data from 60 autistic children receiving therapy, we applied feature selection and cross-validation techniques to identify the most predictive items across two assessment goals: longitudinal therapy tracking and point-in-time severity estimation. For progress monitoring, the framework identified 16 items (21% of the original questionnaire) that retained strong correlation with total score change and full subdomain coverage. We also generated smaller subsets (1-7 items) for efficient approximations. For point-in-time severity assessment, our model achieved over 80% classification accuracy using just 13 items (17% of the original set). While demonstrated on ATEC, the methodology-based on subset optimization, model interpretability, and statistical rigor-is broadly applicable to other high-dimensional psychometric tools. The resulting framework could potentially enable more accessible, frequent, and scalable assessments and offer a data-driven approach for AI-supported interventions across neurodevelopmental and psychiatric contexts.