Machine Learning Framework for Personalized Autism Therapy and Intervention Planning: Extending Impact Beyond Detection into Treatment Support
Posted: Oct 30, 2024
Abstract
The heterogeneity of autism spectrum disorder necessitates personalized intervention approaches, yet current therapy planning relies heavily on clinician experience with limited data-driven decision support. This research presents a comprehensive machine learning framework that extends beyond autism detection to generate
personalized therapy recommendations and predict individual treatment outcomes.
Our system integrates multimodal data from 3,750 children across 45 clinical sites,
including behavioral assessments, therapy session metrics, physiological measurements, and environmental context to create dynamic intervention plans. The framework employs ensemble learning with feature importance weighting to recommend
specific therapeutic strategies, achieving 91.8% accuracy in predicting optimal intervention approaches and 89.3% accuracy in forecasting 6-month developmental
trajectories. Implementation in 22 intervention centers demonstrated significant
improvements in outcomes, with children receiving ML-guided therapy showing
47% greater progress in communication skills and 52% faster achievement of individualized education plan objectives compared to standard care. The system’s
reinforcement learning component continuously adapts recommendations based on
treatment response, reducing ineffective strategy persistence by 68%. Clinical validation with 127 therapists revealed high usability ratings (4.4/5.0) and 89% agreement that the framework enhanced decision-making quality. This research represents a paradigm shift from one-size-fits-all autism interventions to truly personalized approaches that leverage computational power to match therapeutic strategies
with individual characteristics, preferences, and response patterns, ultimately improving efficiency and effectiveness of autism support services
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