DEEP LEARNING-DRIVEN PREDICTION OF DIFFICULT EXTUBATION AND ICU TRANSFER RISK IN HIGH-RISK ANESTHESIA PATIENTS

Abstract

This work proposes a deep learning-based approach to predicting difficult extubation and intensive care unit (ICU) transfer risk for high-risk anesthesia patients with multi-institutional electronic health record (EHR) data and high-resolution physiological waveforms. Traditional methods of perioperative risk assessment are often inadequate in identifying complex and dynamic physiologic changes related to postoperative respiratory deterioration. This limitation is overcome by the proposed methodology which combines hybrid deep learning architectures such as Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCN) for analysis of both static clinical variables and longitudinal bio signals during the surgery. The dataset also includes continuous physiological data like heart rate, respiratory rate, oxygen saturation, medication administration records, and lab results, enabling comprehensive predictive analytics. To ensure consistency of the data across institutions, a comprehensive preprocessing pipeline was used, including z-score normalization, time-aware imputation, and temporal alignment. The hyperparameters of the model were optimized by Bayesian optimization and then split the data into 70/15/15 as training/validation/test sets. Evaluation of performance showed that it had better predictive power than traditional statistical methods, with high discrimination rates expressed by the area under the receiver operating characteristic curve (AUROC), precision-recall analysis, sensitivity, specificity and F1-score. Moreover, explainable AI methods with the help of SHAP values found significant perioperative determinants associated with extubation failure and risk of being transferred to the ICU, which promoted clinical interpretability and trustworthiness. The results indicate that AI-powered perioperative monitoring systems can aid in proactive decision-making, enhance postoperative patient safety, optimize the use of the intensive care unit, and lower the risk of adverse respiratory events. The study underscores the increasing promise of explainable deep learning systems to revolutionize perioperative anesthesia care and move it toward personalized, data-driven care.