  Electronic Health Records (EHR) are high-dimensional data with implicit
connections among thousands of medical concepts. These connections, for
instance, the co-occurrence of diseases and lab-disease correlations can be
informative when only a subset of these variables is documented by the
clinician. A feasible approach to improving the representation learning of EHR
data is to associate relevant medical concepts and utilize these connections.
Existing medical ontologies can be the reference for EHR structures, but they
place numerous constraints on the data source. Recent progress on graph neural
networks (GNN) enables end-to-end learning of topological structures for
non-grid or non-sequential data. However, there are problems to be addressed on
how to learn the medical graph adaptively and how to understand the effect of
the medical graph on representation learning. In this paper, we propose a
variationally regularized encoder-decoder graph network that achieves more
robustness in graph structure learning by regularizing node representations.
Our model outperforms the existing graph and non-graph based methods in various
EHR predictive tasks based on both public data and real-world clinical data.
Besides the improvements in empirical experiment performances, we provide an
interpretation of the effect of variational regularization compared to standard
graph neural network, using singular value analysis.
