Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset. (April 2023)

• Record Type: Journal Article
• Authors: Yun, Won Joon; Shin, MyungJae; Jung, Soyi; Ko, JeongGil; Lee, Hyung-Chul; Kim, Joongheon
• Is Part Of: Computers in biology and medicine. Volume 156(2023)
• Publication Date: 2023-04
• Subjects: Deep reinforcement learning -- Automated drug control computing -- Healthcare IT; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106739
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In this work, we present a deep reinforcement learning-based approach as a baseline system for autonomous propofol infusion control. Specifically, design an environment for simulating the possible conditions of a target patient based on input demographic data and design our reinforcement learning model-based system so that it effectively makes predictions on the proper level of propofol infusion to maintain stable anesthesia even under dynamic conditions that can affect the decision-making process, such as the manual control of remifentanil by anesthesiologists and the varying patient conditions under anesthesia. Through an extensive set of evaluations using patient data from 3000 subjects, we show that the proposed method results in stabilization in the anesthesia state, by managing the bispectral index (BIS) and effect-site concentration for a patient showing varying conditions.