  Deep learning models exhibit state-of-the-art performance for many predictive
healthcare tasks using electronic health records (EHR) data, but these models
typically require training data volume that exceeds the capacity of most
healthcare systems. External resources such as medical ontologies are used to
bridge the data volume constraint, but this approach is often not directly
applicable or useful because of inconsistencies with terminology. To solve the
data insufficiency challenge, we leverage the inherent multilevel structure of
EHR data and, in particular, the encoded relationships among medical codes. We
propose Multilevel Medical Embedding (MiME) which learns the multilevel
embedding of EHR data while jointly performing auxiliary prediction tasks that
rely on this inherent EHR structure without the need for external labels. We
conducted two prediction tasks, heart failure prediction and sequential disease
prediction, where MiME outperformed baseline methods in diverse evaluation
settings. In particular, MiME consistently outperformed all baselines when
predicting heart failure on datasets of different volumes, especially
demonstrating the greatest performance improvement (15% relative gain in PR-AUC
over the best baseline) on the smallest dataset, demonstrating its ability to
effectively model the multilevel structure of EHR data.
