Reinforcement learning-based control for combined infusion of sedatives and analgesics

Abstract

The focus of several clinical trials and research in the area of clinical pharmacology is to fine tune the drug dosing in the phase of additive, antagonistic, and synergistic drug interactive effects. It is important to consider the interactive effects of the drugs to restrict the drug usage to the optimal level required to achieve certain therapeutic effects. Such optimal drug dosing methods will minimize the adverse drug effects and cost associated with the treatment. In this paper, we discuss the use of a reinforcement learning (RL)-based controller to fine tune the drug titration while different drugs with interactive effects are administrated simultaneously. We demonstrate the efficacy of the method by using 25 simulated patients for the simultaneous infusion of a sedative and analgesic drug which has synergistic interactive effect.