Abstract
Deep learning-based predictive models, leveraging Electronic Health Records (EHR), are receiving increasing attention in healthcare. An effective representation of a patient's EHR should hierarchically encompass both the temporal relationships between historical visits and medical events, and the inherent structural information within these elements. Existing patient representation methods can be roughly categorized into sequential representation and graphical representation. The sequential representation methods focus only on the temporal relationships among longitudinal visits. On the other hand, the graphical representation approaches, while adept at extracting the graph-structured relationships between various medical events, fall short in effectively integrate temporal information. To capture both types of information, we model a patient's EHR as a novel temporal heterogeneous graph. This graph includes historical visits nodes and medical events nodes. It propagates structured information from medical event nodes to visit nodes and utilizes time-aware visit nodes to capture changes in the patient's health status. Furthermore, we introduce a novel temporal graph transformer (TRANS) that integrates temporal edge features, global positional encoding, and local structural encoding into heterogeneous graph convolution, capturing both temporal and structural information. We validate the effectiveness of TRANS through extensive experiments on three real-world datasets. The results show that our proposed approach achieves state-of-the-art performance.
Abstract (translated)
基于深度学习的预测模型，利用电子病历（EHR）越来越受到医疗领域的关注。一个有效的患者EHR的表示应该分层涵盖历史就诊之间的时间关系和医疗事件内部的固有结构信息。现有的患者表示方法可以大致分为序列表示和图形表示。序列表示方法仅关注纵向就诊之间的关系。另一方面，图形表示方法虽然善于提取各种医疗事件之间的图结构关系，但在有效整合时间信息方面存在不足。为了捕捉这两种信息，我们将患者的EHR建模为一种新颖的时间异质图。这个图包括历史就诊节点和医疗事件节点。它从医疗事件节点传播结构信息到就诊节点，并使用时间感知就诊节点来捕捉患者健康状况的改变。此外，我们引入了一种新颖的时间图变换器（TRANS），它将时间边特征、全局位置编码和局部结构编码集成到异质图卷积中，捕捉时间和结构信息。我们对TRANS的有效性进行了广泛实验，涉及三个真实世界数据集。结果表明，我们提出的方法达到了最先进的水平。
URL
https://arxiv.org/abs/2405.03943
