  Effective modeling of electronic health records (EHR) is rapidly becoming an
important topic in both academia and industry. A recent study showed that using
the graphical structure underlying EHR data (e.g. relationship between
diagnoses and treatments) improves the performance of prediction tasks such as
heart failure prediction. However, EHR data do not always contain complete
structure information. Moreover, when it comes to claims data, structure
information is completely unavailable to begin with. Under such circumstances,
can we still do better than just treating EHR data as a flat-structured
bag-of-features? In this paper, we study the possibility of jointly learning
the hidden structure of EHR while performing supervised prediction tasks on EHR
data. Specifically, we discuss that Transformer is a suitable basis model to
learn the hidden EHR structure, and propose Graph Convolutional Transformer,
which uses data statistics to guide the structure learning process. The
proposed model consistently outperformed previous approaches empirically, on
both synthetic data and publicly available EHR data, for various prediction
tasks such as graph reconstruction and readmission prediction, indicating that
it can serve as an effective general-purpose representation learning algorithm
for EHR data.
