A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling
This is an incremental framework for researchers exploring simulation-driven digital twins in healthcare, but it lacks clinical readiness or strong performance claims.
The paper presents a proof-of-concept digital twin framework for diabetes modeling that generates interpretable simulated trajectories using benchmark data and synthetic augmentation, demonstrating feasibility for decision-aware analysis without clinical validation.
This paper presents a proof-of-concept digital twin framework for simulation-driven diabetes modeling using benchmark clinical data, synthetic temporal augmentation, and illustrative continuous glucose monitoring (CGM) analysis. Unlike traditional predictive models, the framework focuses on generating interpretable simulated trajectories rather than clinically validated outcomes. Evaluation is conducted using a public dataset combined with controlled synthetic scenarios to illustrate temporal behavior and intervention effects. Results illustrate the feasibility of integrating prediction with counterfactual simulation for decision-aware analysis. This work does not claim clinical readiness but provides a foundation for future research on simulation-driven digital twin systems in healthcare.