Luo Rutao, Piovoso Michael J, Zurakowski Ryan
PhD student in Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
Professor of Electrical Engineering, Penn State University Great Valley, 30E. Swedesford Road, Malvern, PA 19355, USA
Proc Am Control Conf. 2010;2010:5155-5160. doi: 10.1109/acc.2010.5530483.
In previous work, we have developed optimal-control based approaches that seek to minimize the risk of subsequent virological failure by "pre-conditioning" the viral load during therapy switches. In this paper, we use Monte-Carlo methods to evaluate the sensitivity of an open-loop implementation of these approaches to modeling errors. To account for hidden parameter dependencies, we use parameter distributions obtained from the convergence of Bayesian parameter estimation techniques applied to sets of clinical data obtained during serial therapy interruptions as the distribution from which the Monte-Carlo method samples.
在之前的工作中，我们开发了基于最优控制的方法，旨在通过在治疗转换期间对病毒载量进行“预处理”来最小化后续病毒学失败的风险。在本文中，我们使用蒙特卡罗方法来评估这些方法的开环实现对建模误差的敏感性。为了考虑隐藏的参数依赖性，我们使用从应用于连续治疗中断期间获得的临床数据集的贝叶斯参数估计技术的收敛中获得的参数分布，作为蒙特卡罗方法采样的分布。
