Anesthesia depth prediction from drug infusion history using hybrid AI

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.