In recent years, medical information technology has made it possible for electronic health record (EHR) to store fairly complete clinical data. This has brought health care into the era of "big data". However, medical data are often sparse and strongly correlated, which means that medical problems cannot be solved effectively. With the rapid development of deep learning in recent years, it has provided opportunities for the use of big data in healthcare. In this paper, we propose a temporal-saptial correlation attention network (TSCAN) to handle some clinical characteristic prediction problems, such as predicting death, predicting length of stay, detecting physiologic decline, and classifying phenotypes. Based on the design of the attention mechanism model, our approach can effectively remove irrelevant items in clinical data and irrelevant nodes in time according to different tasks, so as to obtain more accurate prediction results. Our method can also find key clinical indicators of important outcomes that can be used to improve treatment options. Our experiments use information from the Medical Information Mart for Intensive Care (MIMIC-IV) database, which is open to the public. Finally, we have achieved significant performance benefits of 2.0\% (metric) compared to other SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate, 45.1\% on length of stay. The source code can be find: \url{https://github.com/yuyuheintju/TSCAN}.

翻译：近年来，医疗信息技术使电子健康记录（EHR）能够存储相当完整的临床数据，这推动医疗进入“大数据”时代。然而，医疗数据往往具有稀疏性和强相关性，导致医疗问题难以有效解决。随着近年来深度学习的快速发展，这为大数据在医疗领域的应用提供了契机。本文提出一种时空关联注意力网络（TSCAN），用于处理死亡预测、住院时长预测、生理衰退检测及表型分类等临床特征预测问题。基于注意力机制模型的设计，我们的方法能根据不同任务有效去除临床数据中的无关项和时间序列中的无关节点，从而获得更准确的预测结果。该方法还能发现关键临床指标，这些指标对改善治疗方案具有重要意义。实验采用公开的医疗信息重症监护数据集（MIMIC-IV），最终在各项指标上相比其他最先进预测方法取得了2.0%的显著性能提升，其中死亡率预测达到90.7%，住院时长预测达到45.1%。源代码获取地址：\url{https://github.com/yuyuheintju/TSCAN}。