  Distributed representations of medical concepts have been used to support
downstream clinical tasks recently. Electronic Health Records (EHR) capture
different aspects of patients' hospital encounters and serve as a rich source
for augmenting clinical decision making by learning robust medical concept
embeddings. However, the same medical concept can be recorded in different
modalities (e.g., clinical notes, lab results)-with each capturing salient
information unique to that modality-and a holistic representation calls for
relevant feature ensemble from all information sources. We hypothesize that
representations learned from heterogeneous data types would lead to performance
enhancement on various clinical informatics and predictive modeling tasks. To
this end, our proposed approach makes use of meta-embeddings, embeddings
aggregated from learned embeddings. Firstly, modality-specific embeddings for
each medical concept is learned with graph autoencoders. The ensemble of all
the embeddings is then modeled as a meta-embedding learning problem to
incorporate their correlating and complementary information through a joint
reconstruction. Empirical results of our model on both quantitative and
qualitative clinical evaluations have shown improvements over state-of-the-art
embedding models, thus validating our hypothesis.
