  Computational prediction of in-hospital mortality in the setting of an
intensive care unit can help clinical practitioners to guide care and make
early decisions for interventions. As clinical data are complex and varied in
their structure and components, continued innovation of modeling strategies is
required to identify architectures that can best model outcomes. In this work,
we train a Heterogeneous Graph Model (HGM) on Electronic Health Record data and
use the resulting embedding vector as additional information added to a
Convolutional Neural Network (CNN) model for predicting in-hospital mortality.
We show that the additional information provided by including time as a vector
in the embedding captures the relationships between medical concepts, lab
tests, and diagnoses, which enhances predictive performance. We find that
adding HGM to a CNN model increases the mortality prediction accuracy up to
4\%. This framework serves as a foundation for future experiments involving
different EHR data types on important healthcare prediction tasks.
