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基于积分强化学习的药物剂量最优自适应控制。

Optimal adaptive control of drug dosing using integral reinforcement learning.

机构信息

Department of Electrical Engineering, Qatar University, Qatar.

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0150, USA.

出版信息

Math Biosci. 2019 Mar;309:131-142. doi: 10.1016/j.mbs.2019.01.012. Epub 2019 Feb 5.

Abstract

In this paper, a reinforcement learning (RL)-based optimal adaptive control approach is proposed for the continuous infusion of a sedative drug to maintain a required level of sedation. To illustrate the proposed method, we use the common anesthetic drug propofol used in intensive care units (ICUs). The proposed online integral reinforcement learning (IRL) algorithm is designed to provide optimal drug dosing for a given performance measure that iteratively updates the control solution with respect to the pharmacology of the patient while guaranteeing convergence to the optimal solution. Numerical results are presented using 10 simulated patients that demonstrate the efficacy of the proposed IRL-based controller.

摘要

本文提出了一种基于强化学习(RL)的最佳自适应控制方法,用于持续输注镇静药物以维持所需的镇静水平。为了说明所提出的方法,我们使用了重症监护病房(ICU)中常用的麻醉药物异丙酚。所提出的在线积分强化学习(IRL)算法旨在为给定的性能指标提供最佳的药物剂量,该算法根据患者的药理学进行控制解的迭代更新,同时保证收敛到最优解。使用 10 个模拟患者的数值结果证明了所提出的基于 IRL 的控制器的有效性。

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