A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling

TL;DR

This paper introduces a proof-of-concept digital twin framework for diabetes modeling that emphasizes simulation-driven, interpretable trajectories over traditional predictive outcomes, using benchmark data and synthetic scenarios.

Contribution

The work presents a novel simulation-driven digital twin framework for diabetes, integrating prediction with counterfactual analysis for decision-aware healthcare modeling.

Findings

Feasibility of combining prediction with counterfactual simulation demonstrated.

Framework can generate interpretable simulated trajectories.

Evaluation using public data and synthetic scenarios shows potential for decision support.

Abstract

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.