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.

本文评估了基于 transformer 的深度学习模型在术中麻醉管理数据方面预测术后急性肾功能衰竭、术后肺部并发症和术后住院死亡的表现，并比较其与传统机器学习模型如梯度提升树和序列注意模型等的表现。结果表明，transformer 式模型在预测术后并发症方面具有卓越的表现，突显了深度学习技术特别是 transformer 式模型在改变卫生保健行业术后护理方法方面的潜力。

使用 Transformers 预测术后肾脏和肺部并发症