用模拟推断实现1型糖尿病实时数字孪生，提升参数估计速度与泛化能力。
A Real-Time Digital Twin for Type 1 Diabetes using Simulation-Based Inference
- 基于神经后验估计的模拟推断，实现参数快速推断。
- 在未见条件下仍保持高精度，推理速度显著优于传统方法。
- 适合临床实时监测与个性化治疗决策支持场景。
准确估计生理模型参数是构建可靠数字孪生的关键。对于1型糖尿病，由于葡萄糖-胰岛素相互作用复杂，这一任务尤为困难。传统基于马尔可夫链蒙特卡洛的方法在高维参数空间中表现不佳，且需在推理时从头拟合参数，导致计算成本高、速度慢。本文提出一种基于神经后验估计的模拟推断（Simulation-Based Inference, SBI）方法，高效捕捉进餐、胰岛素与血糖水平间的复杂关系，实现可复用的快速推理。实验表明，SBI不仅在参数估计上优于传统方法，还能更好泛化至未见情境，在保证可靠不确定性量化的同时实现实时后验推断。
原文摘要 · Abstract (English)
Accurately estimating parameters of physiological models is essential to achieving reliable digital twins. For Type 1 Diabetes, this is particularly challenging due to the complexity of glucose-insulin interactions. Traditional methods based on Markov Chain Monte Carlo struggle with high-dimensional parameter spaces and fit parameters from scratch at inference time, making them slow and computationally expensive. In this study, we propose a Simulation-Based Inference approach based on Neural Posterior Estimation to efficiently capture the complex relationships between meal intake, insulin, and glucose level, providing faster, amortized inference. Our experiments demonstrate that SBI not only outperforms traditional methods in parameter estimation but also generalizes better to unseen conditions, offering real-time posterior inference with reliable uncertainty quantification.
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