With the rise of deep learning, several recent studies on deep learning-based methods for electronic health records (EHR) successfully address real-world clinical challenges by utilizing effective representations of medical entities. However, existing EHR representation learning methods that focus on only diagnosis codes have limited clinical value, because such structured codes cannot concretely describe patients' medical conditions, and furthermore, some of the codes assigned to patients contain errors and inconsistency; this is one of the well-known caveats in the EHR. To overcome this limitation, in this paper, we fuse more detailed and accurate information in the form of natural language provided by unstructured clinical data sources (i.e., clinical notes). We propose HORDE, a unified graph representation learning framework to embed heterogeneous medical entities into a harmonized space for further downstream analyses as well as robustness to inconsistency in structured codes. Our extensive experiments demonstrate that HORDE significantly improves the performances of conventional clinical tasks such as subsequent code prediction and patient severity classification compared to existing methods, and also show the promising results of a novel EHR analysis about the consistency of each diagnosis code assignment.

中文翻译：

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动态EHR图上的协调表示学习。

随着深度学习的兴起，有关基于深度学习的电子健康记录（EHR）方法的一些最新研究通过利用医学实体的有效表示成功解决了现实世界中的临床挑战。然而，现有的仅关注诊断代码的EHR表示学习方法具有有限的临床价值，因为这样的结构化代码无法具体描述患者的医疗状况，此外，分配给患者的某些代码存在错误和不一致。这是《电子病历》中著名的警告之一。为克服此限制，在本文中，我们以非结构化临床数据源（即临床说明）提供的自然语言形式融合了更详细，准确的信息。我们建议部落，统一的图形表示学习框架，可将异构医疗实体嵌入到统一的空间中，以进行进一步的下游分析以及结构化代码不一致的鲁棒性。我们广泛的实验表明，与现有方法相比，HORDE显着改善了常规临床任务的性能，例如后续代码预测和患者严重性分类，并且还显示了有关每个诊断代码分配一致性的新颖EHR分析的有希望的结果。