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Accurately predicting anesthetic effects is essential for target-controlledinfusion systems. The traditional (PK-PD) models for Bispectral index (BIS)prediction require manual selection of model parameters, which can bechallenging in clinical settings. Recently proposed deep learning methods canonly capture general trends and may not predict abrupt changes in BIS. Toaddress these issues, we propose a transformer-based method for predicting thedepth 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 anattention mechanism to discover the interactions between the drugs. We also uselabel distribution smoothing and reweighting losses to address data imbalance.Experimental results show that our proposed method outperforms traditionalPK-PD models and previous deep learning methods, effectively predictinganesthetic depth under sudden and deep anesthesia conditions.

A Transformer-based Prediction Method for Depth of Anesthesia During Target-controlled Infusion of Propofol and Remifentanil