Harmonized representation learning on dynamic EHR graphs.

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.