Neurosymbolic Digital Twin for Cardiovascular Disease Prediction and Personalized Modeling
Muhammad Adnan, Yang Yi, Niyaz Ahmad Wani, Shrooq Alsenan, Muhammad Attique Khan, Muhammad Shahid Anwar
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.