Anesthesia depth prediction from drug infusion history using hybrid AI

Abstract

Background: Accurately predicting the depth of anesthesia is essential for ensuring patient safety and optimizing surgical outcomes. Traditional regression-based approaches often struggle to model the complex and dynamic nature of patient responses to anesthetic agents. Machine learning techniques offer a promising alternative by capturing intricate relationships within physiological data. This study proposes a hybrid model integrating Long Short-Term Memory (LSTM) networks, Transformer architectures, and Kolmogorov-Arnold Networks (KAN) to improve the predictive accuracy of anesthesia depth.

Methods: The proposed model combines multiple deep learning techniques to address different aspects of anesthesia prediction. The LSTM component captures the sequential nature of drug administration and physiological responses. The Transformer architecture utilizes attention mechanisms to enhance contextual understanding of patient data. The KAN models nonlinear relationships between drug infusion histories and anesthesia depth. The model was trained and evaluated on patient data from a publicly available anesthesia monitoring database. Performance was assessed using Mean Squared Error (MSE) and compared against other models.

Results: The hybrid model demonstrated superior predictive performance compared to conventional regression approaches. Tested on the VitalDB database, the proposed framework achieved a MSE of 0.0062, which is lower than other methods. The inclusion of attention mechanisms and nonlinear modeling contributed to improved accuracy and robustness. The results indicate that the combined approach effectively captures the temporal and nonlinear characteristics of anesthesia depth, offering a more reliable predictive tool for clinical use.

Conclusions: This study presents a novel deep learning framework for anesthesia depth prediction, integrating sequential, attention-based, and nonlinear modeling techniques. The results suggest that this hybrid approach enhances prediction reliability and provides anesthesiologists with a more comprehensive analysis of factors influencing anesthesia depth. Future research will focus on refining model robustness, exploring real-time applications, and addressing potential biases in predictive analytics to further improve clinical decision-making.

Keywords: Deep learning; Depth of anesthesia; Drug infusion history; Kolmogorov-Arnold Networks; LSTM; Transformers.

Fig. 1

Visualization of BIS for CaseID 1210

Fig. 2

Overview of the model architechture

Fig. 3

Visualization of benchmarking models and our architecture for predicting the BIS for two cases with IDs of 1210 and 1392

Fig. 4

Performance visualization on the test dataset in ablation study