Optimal adaptive control of drug dosing using integral reinforcement learning

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.

Keywords: Anesthesia administration; Drug dosing; Optimal adaptive control; Reinforcement learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anesthetics, Intravenous / administration & dosage*
  • Anesthetics, Intravenous / pharmacokinetics
  • Computer Simulation
  • Drug Dosage Calculations*
  • Humans
  • Infusions, Parenteral*
  • Machine Learning*
  • Models, Biological*
  • Propofol / administration & dosage*
  • Propofol / pharmacokinetics

Substances

  • Anesthetics, Intravenous
  • Propofol