A Machine Learning-Based Framework to Shorten the Questionnaire for Assessing Autism Intervention
This addresses the problem of assessment burden for caregivers of individuals with autism, enabling more accessible monitoring, though it is incremental as it builds on existing psychometric tools.
The study tackled the burden of the 77-item Autism Treatment Evaluation Checklist (ATEC) for caregivers by developing a machine learning framework that shortens it to 16 items (21% of original) for therapy tracking and 13 items (17% of original) for severity assessment with over 80% accuracy.
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.