用仿真构建可解释的糖尿病数字孪生，辅助决策分析。
A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling
- 基于临床数据与合成时序增强构建仿真框架
- 实现干预效果的反事实模拟与时间行为分析
- 适合医疗仿真与智能决策研究者参考
本文提出一个糖尿病建模的数字孪生概念验证框架，采用基准临床数据、合成时序增强及连续血糖监测（CGM）示例分析。与传统预测模型不同，该框架聚焦于生成可解释的模拟轨迹，而非临床验证结果。通过公开数据集与受控合成场景评估，展示其在模拟时间动态与干预效应方面的可行性。研究表明，该方法可实现预测与反事实仿真的融合，支持决策导向分析。本文不宣称具备临床应用能力，但为未来基于仿真的医疗数字孪生系统研究奠定基础。
原文摘要 · Abstract (English)
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.
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