AI 中文总结
针对1型糖尿病的生理模型，本文提出RBSVGD滤波框架实现状态-参数联合推断，经验证其血糖与参数估计性能优于EKF和AEKF。
AI 中文摘要
血糖估计是基于模型的决策支持（DS）和自动胰岛素输注（AID）系统的核心。依赖生理/隔室模型的控制系统严重依赖模型参数化，参数要么使用群体值定义，要么通过用户数据进行个性化调整，通常被定义为常数。然而，在真实的自由生活条件下，固定参数会限制血糖水平和状态的准确重建与估计。本文针对1型糖尿病（T1D）的非线性时变生理模型，提出并讨论了一种用于在线联合状态估计和参数辨识的递归滤波框架，具体采用Rao-Blackwellized Stein变分梯度下降（RBSVGD）滤波器计算模型状态和参数的联合后验分布。将该方法应用于Hovorka血糖-胰岛素模型，并使用俄勒冈健康与科学大学（OHSU）模拟器生成的20名虚拟患者数据进行验证，与两种方法对比：（i）采用固定模型参数的标准扩展卡尔曼滤波（EKF）；（ii）用于联合状态-参数估计的增广扩展卡尔曼滤波（AEKF）。结果表明，所提基于RBSVGD的框架在血糖估计精度和模型参数估计方面均优于EKF和AEKF方法。
英文摘要
Blood glucose estimation is the cornerstone of model-based decision support (DS) and Automated Insulin Delivery (AID) systems. Control systems that rely on physiologic/compartmental models depend heavily on model parameterization, which is either defined using population values or personalized through the user's data. Often, the model parameters are defined as constants. However, under real-world free-living conditions, fixed parameters can limit the accurate reconstruction and estimation of glucose levels and states. In this paper, we propose and discuss a recursive filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying physiological models for Type 1 Diabetes (T1D). Specifically, we employ a Rao-Blackwellized Stein Variational Gradient Descent (RBSVGD) filter to compute the joint posterior distributions of model states and parameters. The proposed approach is applied to the Hovorka glucose-insulin model and validated using data generated by the the Oregon Health & Science University (OHSU) simulator across 20 virtual patients. We perform a comparative analysis against: (i) a standard Extended Kalman Filter (EKF) with fixed model parameters, and (ii) an Augmented Extended Kalman Filter (AEKF) for joint state-parameter estimation. The results demonstrate that the proposed RBSVGD-based framework outperforms both EKF and AEKF approaches not only in terms of the accuracy of glucose estimation, but also in terms of estimated model parameters.
Comments7 pages, 3 figures. Accepted for publication at the 65th IEEE Conference on Decision and Control (CDC 2026)
