Anesthesia depth prediction from drug infusion history using hybrid AI.
Journal:
BMC medical informatics and decision making
PMID:
40200239
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.