Neurosymbolic Digital Twin for Cardiovascular Disease Prediction and Personalized Modeling

Abstract

Cardiovascular prediction and therapy planning require high diagnostic fidelity, identifiable causal structure, patient-specific adaptation, and quantifiable privacy. NeuroTwin is a neurosymbolic digital twin that integrates four computational modules into a unified clinical decision framework. The adaptive diffusion transformer (ADViT) performs modality-specific denoising of ECG and PCG signals, followed by patch-level feature encoding and cross-modal fusion that preserves temporal-spectral structure. The symbolic causal discovery network (SCDN) constructs a sparse directed acyclic graph through a differentiable acyclicity constraint and converts stable edges into executable rules. The neural federated digital twin (NFDT) performs distributed optimization with differentially private Gaussian aggregation and incorporates online patient-state updates for personalized modeling under heterogeneous institutional data distributions. A hierarchical meta-reinforcement learner (HMRL) governs treatment recommendations through a bi-level policy that balances symptom reduction, adverse-effect mitigation, and longitudinal stability. NeuroTwin achieves 98.5% diagnostic precision, 96.2% success in treatment optimization, a 0.942 causal explainability score and a 0.032 privacy leakage rate.

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