Forecasting anesthetic depth using an auto-regressive transformer in propofol infusion during the induction phase.

Journal: Journal of anesthesia
Published Date: Jun 16, 2025

Abstract

PURPOSE: Forecasting the depth of anesthesia (DOA) is significant for adjusting the dosage of propofol infusion during total intravenous anesthesia. Traditional pharmacokinetic (PK) and pharmacodynamic (PD) models may lack precision in DOA forecasting. Thus, machine learning models optimized to fit the clinical data have been adopted to predict the DOA, showing improved accuracy. These earlier approaches followed the recurrent and/or the feed-forward framework that generates the predictions based on the patient's demographic data. In this work, to explore the potential advantages of using real-time information in DOA forecasting, we proposed using the auto-regressive (AR) frameworks to improve the accuracy of DOA prediction during the induction phase of anesthesia.

Authors

  • Chen-Hsiang Chi
    Department of Anesthesiology, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
  • Guan-Ju Peng
    Graduate Institute of Data Science and Information Computing, National Chung Hsing University, No.145, Xingda Rd., South Dist., Taichung City, 402, Taiwan (R.O.C.). gjpeng@email.nchu.edu.tw.
  • Yuan-Ji Day
    Department of Anesthesiology, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
  • Che-Hao Hsu
    Department of Anesthesiology, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
  • Michael J Sheen
    Department of Anesthesiology, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals