A Transformer-based Prediction Method for Depth of Anesthesia During
  Target-controlled Infusion of Propofol and Remifentanil
Yongkang He Siyuan Peng Mingjin Chen Zhijing Yang Yuanhui Chen
Accurately predicting anesthetic effects is essential for target-controlled
infusion systems. The traditional (PK-PD) models for Bispectral index (BIS)
prediction require manual selection of model parameters, which can be
challenging in clinical settings. Recently proposed deep learning methods can
only capture general trends and may not predict abrupt changes in BIS. To
address these issues, we propose a transformer-based method for predicting the
depth of anesthesia (DOA) using drug infusions of propofol and remifentanil.
Our method employs long short-term memory (LSTM) and gate residual network
(GRN) networks to improve the efficiency of feature fusion and applies an
attention mechanism to discover the interactions between the drugs. We also use
label distribution smoothing and reweighting losses to address data imbalance.
Experimental results show that our proposed method outperforms traditional
PK-PD models and previous deep learning methods, effectively predicting
anesthetic depth under sudden and deep anesthesia conditions.