  Extracting actionable insight from Electronic Health Records (EHRs) poses
several challenges for traditional machine learning approaches. Patients are
often missing data relative to each other; the data comes in a variety of
modalities, such as multivariate time series, free text, and categorical
demographic information; important relationships among patients can be
difficult to detect; and many others. In this work, we propose a novel approach
to address these first three challenges using a representation learning scheme
based on message passing. We show that our proposed approach is competitive
with or outperforms the state of the art for predicting in-hospital mortality
(binary classification), the length of hospital visits (regression) and the
discharge destination (multiclass classification).
