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.

翻译：术后并发症是医疗行业面临的重大挑战，会导致医疗费用增加、住院时间延长，少数情况下甚至造成患者死亡。为改善患者预后并降低医疗成本，医疗服务提供者依赖各种围手术期风险评分来指导临床决策并确定护理优先级。近年来，机器学习技术在预测术后并发症和死亡率方面展现出潜力，深度学习模型在医疗应用中取得了显著成功。然而，将深度学习模型应用于术中麻醉管理数据的研究仍然有限。本文评估了基于Transformer的模型在预测术后急性肾衰竭、术后肺部并发症以及术后院内死亡率方面的表现。我们将该方法的表现与包括梯度提升树和序列注意力模型在内的最新表格数据预测模型在临床数据集上进行了比较。结果表明，基于Transformer的模型在预测术后并发症方面能够取得更优表现，并优于传统机器学习模型。本工作凸显了深度学习技术，特别是基于Transformer的模型，在革新医疗行业术后护理方法方面的潜力。