  Postoperative complications pose a significant challenge in the healthcare
industry, resulting in elevated healthcare expenses and prolonged hospital
stays, and in rare instances, patient mortality. To improve patient outcomes
and reduce healthcare costs, healthcare providers rely on various perioperative
risk scores to guide clinical decisions and prioritize care. In recent years,
machine learning techniques have shown promise in predicting postoperative
complications and fatality, with deep learning models achieving remarkable
success in healthcare applications. However, research on the application of
deep learning models to intra-operative anesthesia management data is limited.
In this paper, we evaluate the performance of transformer-based models in
predicting postoperative acute renal failure, postoperative pulmonary
complications, and postoperative in-hospital mortality. We compare our method's
performance with state-of-the-art tabular data prediction models, including
gradient boosting trees and sequential attention models, on a clinical dataset.
Our results demonstrate that transformer-based models can achieve superior
performance in predicting postoperative complications and outperform
traditional machine learning models. This work highlights the potential of deep
learning techniques, specifically transformer-based models, in revolutionizing
the healthcare industry's approach to postoperative care.
