AI Article Synopsis
- - The study introduces a deep reinforcement learning approach for controlling propofol infusion autonomously, aiming to enhance anesthesia management.
- - It involves creating a simulation environment that adapts to different patient demographics and conditions, allowing for effective predictions on propofol levels necessary for stable anesthesia.
- - Evaluations using data from 3,000 patients demonstrate that the system successfully stabilizes anesthesia by managing key indicators like bispectral index (BIS) and effect-site concentration, even amidst fluctuating patient conditions.
Article 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.
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| http://dx.doi.org/10.1016/j.compbiomed.2023.106739 | DOI Listing |
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