Neurosymbolic Digital Twin for Cardiovascular Disease Prediction and Personalized Modeling

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Computational Cardiology

Background:

• Cardiovascular disease management necessitates accurate diagnostics, causal understanding, personalized treatments, and data privacy.
• Existing clinical decision frameworks often lack integration of these critical components.

Purpose of the Study:

• To introduce NeuroTwin, a neurosymbolic digital twin designed for unified cardiovascular clinical decision-making.
• To evaluate NeuroTwin's efficacy in diagnostic precision, treatment optimization, causal explainability, and privacy preservation.

Main Methods:

• NeuroTwin integrates four modules: adaptive diffusion transformer (ADViT) for signal denoising and fusion, symbolic causal discovery network (SCDN) for causal graph construction, neural federated digital twin (NFDT) for private distributed learning, and hierarchical meta-reinforcement learner (HMRL) for treatment recommendations.
• ADViT utilizes patch-level encoding and cross-modal fusion for ECG/PCG signals.
• SCDN employs differentiable acyclicity constraints for rule generation.
• NFDT uses differentially private Gaussian aggregation for federated learning.
• HMRL implements a bi-level policy for treatment optimization.

Main Results:

• NeuroTwin demonstrated 98.5% diagnostic precision and 96.2% success in treatment optimization.
• A causal explainability score of 0.942 was achieved.
• The system maintained a low privacy leakage rate of 0.032.

Conclusions:

• NeuroTwin offers a robust, integrated framework for advanced cardiovascular prediction and therapy planning.
• The system effectively balances diagnostic accuracy, treatment personalization, causal transparency, and data privacy.
• NeuroTwin represents a significant advancement in AI-driven clinical decision support for cardiovascular care.