Time series prediction for monitoring cardiovascular health in autistic patients.

INTRODUCTION

Monitoring cardiovascular health in autistic patients presents unique challenges due to atypical sensory profiles, altered autonomic regulation, and communication difficulties. As cardiovascular comorbidities rise in this population, there is an urgent need for tailored computational strategies to enable continuous monitoring and predictive care planning. Traditional time series methods-including statistical autoregressive models and recurrent neural networks-are constrained by opaque decision processes, limited personalization, and insufficient handling of multimodal data, restricting their utility where transparency and individualized modeling are critical.

METHODS

We introduce a structurally-aware, semantically-grounded framework for time series prediction tailored to cardiovascular trajectories in autistic patients. Our approach departs from black-box modeling by integrating symbolic clinical abstractions, causal event dynamics, and intervention-response coupling within a graph-based paradigm. Central to our method is the CardioGraph Synaptic Encoder (CGSE), a generative model that fuses multimodal data-such as ECG waveforms, blood pressure signals, and structured clinical annotations-into a unified latent space. The CGSE employs dual-level temporal attention to capture patient-specific micro-patterns and population-level structures. To improve generalization and robustness, we propose the Dynamic Cardiovascular Trajectory Alignment (DCTA), which combines task-adaptive curriculum learning with multi-resolution consistency loss.

RESULTS

Our approach effectively addresses challenges such as scarcity of labeled data and clinical heterogeneity common in autistic populations. Experimental results demonstrate that our system significantly outperforms baselines in predictive accuracy, temporal coherence, and interpretability.

DISCUSSION

This work offers a novel, clinically-aligned pipeline for real-time cardiovascular risk monitoring in autistic individuals. By advancing personalized and interpretable healthcare analytics, our method has the potential to support more accurate and transparent decision-making in cardiovascular care pathways for this vulnerable population.