Time series prediction for monitoring cardiovascular health in autistic patients

Abstract

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.

Keywords: autistic patients; cardiovascular health monitoring; graph neural networks; symbolic modeling; time series prediction.

Figure 1

Diagram of the CardioGraph Synaptic Encoder (CGSE) model architecture, showcasing the multi-stage graph-based encoding framework that integrates hierarchical node initialization, bi-temporal consistency, contextual influence simulation, and dynamic temporal attention for capturing evolving cardiophysiological dynamics.

Figure 2

Diagram illustrating the bi-temporal consistency. It shows the integration of temporal dependencies through a bi-temporal fusion mechanism, utilizing forward and backward context vectors to capture physiological changes over time. The final state representation at each time step incorporates both the raw node features and the temporal context from past and future steps, ensuring temporally consistent and physiologically meaningful embeddings.

Figure 3

The architecture of the Dynamic Cardiovascular Trajectory Alignment (DCTA) model. It employs multi-stage embedding strategies and progressive alignment techniques to model cardiovascular trajectory evolution, incorporating temporal coherence, structural alignment, and event-driven trajectory control for more accurate predictions across diverse patient datasets.

Figure 4

Flowchart illustrating the step-by-step construction of the cardiovascular graph used in our model. The process combines multimodal clinical data, semantic typing, temporal sequencing, and integration with medical knowledge. The resulting graph is validated via expert review and temporal filtering before being passed to the CardioGraph Synaptic Encoder.

Figure 5

Schematic diagram of event-driven trajectory control. The diagram illustrates a model for event-driven trajectory control, where a direct encoder processes an input X and passes it through a state encoding network. The encoded state is then influenced by an intervention $a_j$ , with the intervention’s impact on the system captured through the difference ${\delta }_{j\to t^{\prime }}$ between the predicted and actual state at future time $t^{\prime }$ . The model further processes this through a state transition and image decoder to output the predicted state and trajectory. The overall loss function involves several components that enforce the alignment of predicted states with real-world clinical events, smooth propagation over graph-based patient data, and adherence to known disease progression pathways.

Figure 6

Visual explanation of a cardiovascular risk prediction case. The top-left panel shows the raw ECG and blood pressure time series, with high-attention regions shaded in red. The top-right heatmap displays the attention weights over time and signal channels. The bottom-left panel visualizes causal links between past events and predicted risk via graph-based encoding. The bottom-right plot compares predicted risk trajectories under two scenarios: with and without the recommended intervention. This interpretability interface allows clinicians to trace prediction rationale and assess counterfactual outcomes.