摘要: 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 modelling strategies is required to identify
architectures that can best model outcomes. In this work, we trained a Heterogeneous Graph Model (HGM)
on electronic health record (EHR) data and used 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 captured the relationships
between medical concepts, lab tests, and diagnoses, which enhanced predictive performance. We found that
adding HGM to a CNN model increased the mortality prediction accuracy up to 4%. This framework served
as a foundation for future experiments involving different EHR data types on important healthcare prediction
tasks.