自适应不确定性感知建模与随机径向基函数模型预测控制在个性化液体复苏中的应用
Adaptive Uncertainty-Aware Modeling and Stochastic Radial Basis Function Predictive Control for Personalized Fluid Resuscitation
- Kent State University（肯特州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出融合贝叶斯建模与随机径向基函数MPC的框架，实现不确定性感知的个性化液体复苏，通过在线自适应提升MAP调节的稳定性与风险控制。
AI中文摘要：
本文提出了一种新颖框架，将贝叶斯生理建模与最优控制策略相结合，以实现液体复苏过程中不确定性感知的个性化血流动力学调节。首先开发了一种不确定性感知变分自编码器状态空间模型（UVAE-SSM），利用有限数据捕捉平均动脉压（MAP）与液体输注之间的动态关系，同时显式建模偶然不确定性（即测量中的随机性，如传感器噪声）。随后，利用贝叶斯神经网络（BNNs）开发了贝叶斯非线性状态空间模型（BNSSM），以捕捉由生理和患者特异性变异引起的认知不确定性，从而能够创建虚拟患者生成器（VPG）。在此不确定性感知建模框架的基础上，设计了一种随机径向基函数模型预测控制（sRBF-MPC）算法，以在满足生理约束的同时跟踪MAP目标。最后，开发了一种在线微调算法，利用流式VPG数据自适应调整标称UVAE-SSM，从而在闭环治疗过程中实现渐进式个性化。在未见过的动物受试者和独立的人类临床数据集上的仿真结果表明，UVAE-SSM和BNSSM模型具有强大的预测准确性和跨群体泛化能力。闭环评估证实，所提出的sRBF-MPC框架实现了稳定的MAP调节，同时与二次型MPC（Q-MPC）和随机二次型MPC（sQ-MPC）相比，提供了更好的风险感知控制。总体而言，所提出的框架通过在线模型自适应考虑了患者间和患者内的变异性，为重症监护中不确定性感知的个性化血流动力学建模与控制迈出了有前景的一步。
英文摘要：
This paper presents a novel framework integrating Bayesian physiological modeling with optimal control strategies to achieve uncertainty-aware, personalized hemodynamic regulation during fluid resuscitation. An uncertainty-aware variational autoencoder state-space model (UVAE-SSM) was first developed to capture the dynamical relationship between mean arterial pressure (MAP) and fluid infusion using limited data, while explicitly modeling aleatoric uncertainty (i.e., randomness in the measurements, such as sensor noise). Then, a Bayesian nonlinear state-space model (BNSSM) was developed by utilizing Bayesian neural networks (BNNs) to capture epistemic uncertainty arising from physiological and patient-specific variability, enabling the creation of a virtual patient generator (VPG). Building on this uncertainty-aware modeling framework, a stochastic radial basis function model predictive control (sRBF-MPC) algorithm was designed to track the MAP target while satisfying physiological constraints. Finally, an online fine-tuning algorithm was developed to adapt the nominal UVAE-SSM using streaming VPG data, enabling progressive personalization during closed-loop therapy. Simulation results across unseen animal subjects and an independent human clinical dataset demonstrated the strong predictive accuracy and cross-population generalizability of the UVAE-SSM and BNSSM models. Closed-loop evaluations confirmed that the proposed sRBF-MPC framework achieved stable MAP regulation while providing better risk-aware control compared to quadratic MPC (Q-MPC) and stochastic quadratic MPC (sQ-MPC). Overall, the proposed framework accounts for inter- and intra-patient variability through online model adaptation, offering a promising step toward uncertainty-aware, personalized hemodynamic modeling and control in critical care.
