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Deep reinforcement learning-based propofol infusion control for anesthesia: A feasibility study with a 3000-subject dataset
  • Yun, Won Joon ;
  • Shin, Myung Jae ;
  • Jung, Soyi ;
  • Ko, Jeong Gil ;
  • Lee, Hyung Chul ;
  • Kim, Joongheon
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Publication Year
2023-04-01
Journal
Computers in Biology and Medicine
Publisher
Elsevier Ltd
Citation
Computers in Biology and Medicine, Vol.156
Keyword
Automated drug control computingDeep reinforcement learningHealthcare IT
Mesh Keyword
Automated drug control computingBaseline systemsConditionDeep reinforcement learningDrug controlFeasibility studiesHealthcare ITLearning-based approachPropofolReinforcement learningsAnesthesiaAnesthesia, IntravenousAnesthetics, IntravenousElectroencephalographyFeasibility StudiesHumansPiperidinesPropofol
All Science Classification Codes (ASJC)
Health InformaticsComputer Science Applications
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.
ISSN
1879-0534
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/33290
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85150079384&origin=inward
DOI
https://doi.org/2-s2.0-85150079384
Journal URL
www.elsevier.com/locate/compbiomed
Type
Article
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1C1C1014905 ), the Ministry of Science and ICT\u2019s ITRC Program supervised by IITP ( IITP-2021-2020-0-01461 ), and the National Research Foundation of Korea ( 2022R1A2C2004869 ).
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