  Representation learning methods that transform encoded data (e.g., diagnosis
and drug codes) into continuous vector spaces (i.e., vector embeddings) are
critical for the application of deep learning in healthcare. Initial work in
this area explored the use of variants of the word2vec algorithm to learn
embeddings for medical concepts from electronic health records or medical
claims datasets. We propose learning embeddings for medical concepts by using
graph-based representation learning methods on SNOMED-CT, a widely popular
knowledge graph in the healthcare domain with numerous operational and research
applications. Current work presents an empirical analysis of various embedding
methods, including the evaluation of their performance on multiple tasks of
biomedical relevance (node classification, link prediction, and patient state
prediction). Our results show that concept embeddings derived from the
SNOMED-CT knowledge graph significantly outperform state-of-the-art embeddings,
showing 5-6x improvement in ``concept similarity" and 6-20\% improvement in
patient diagnosis.
