A Machine Learning-Based Framework to Shorten the Questionnaire for Assessing Autism Intervention

• URL: http://arxiv.org/abs/2510.26808v1
• Date: Wed, 22 Oct 2025 20:26:53 GMT
• Title: A Machine Learning-Based Framework to Shorten the Questionnaire for Assessing Autism Intervention
• Authors: Audrey Dong, Claire Xu, Samuel R. Guo, Kevin Yang, Xue-Jun Kong,
• Abstract summary: This study introduces a generalizable machine learning framework that seeks to shorten assessments while maintaining evaluative accuracy.<n>For progress monitoring, the framework identified 16 items that retained strong correlation with total score change and full subdomain coverage.<n>For point-in-time severity assessment, our model achieved over 80% classification accuracy using just 13 items.
• Score: 6.233625142245272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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