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

Snomed2Vec: Random Walk and Poincar\'e Embeddings of a Clinical Knowledge Base for Healthcare Analytics