A Clinical Theory-Driven Deep Learning Model for Interpretable Autism Severity Prediction

Autism spectrum disorder (ASD) affects a substantial proportion of children worldwide, yet clinical assessment of symptom severity remains resource-intensive and unevenly accessible. Artificial intelligence (AI) has transformative potential to support scalable and timely severity assessment from behavioral data, but existing approaches largely treat autism as a monolithic prediction target and rely on opaque models that are difficult for clinicians to interpret or trust. Moreover, prior multimodal methods typically integrate heterogeneous behavioral signals using ad hoc fusion strategies that are weakly grounded in clinical theory. We propose a clinical theory-driven deep learning model for interpretable autism severity assessment that explicitly operationalizes established clinical constructs into model design. Drawing on autism research, we represent social construct and motor construct as distinct latent components. These components are integrated through a structured cross-modal attention mechanism guided by a learnable alignment mask that encodes soft spatial correspondence priors between visual and kinematic representations. Theory-specific blocks then aggregate aligned tokens into construct embeddings, which are fused via instance-specific theory weights, yielding transparent symptom profiles aligned with clinical reasoning. Comprehensive experiments demonstrate the state-of-the-art performance of our model over existing baselines. Ablation studies validate that performance gains arise from theory-driven design choices. Analysis of the learned theory weights reveals systematic relationships between symptom profiles and severity, providing empirical support for the multidimensional structure of autism. This work demonstrates how clinical theory can be instantiated as empirically testable architectural designs in deep learning models, advancing both predictive utility and interpretability in healthcare AI systems.