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Biomedical association studies are increasingly done using clinical concepts,and in particular diagnostic codes from clinical data repositories asphenotypes. Clinical concepts can be represented in a meaningful, vector spaceusing word embedding models. These embeddings allow for comparison betweenclinical concepts or for straightforward input to machine learning models.Using traditional approaches, good representations require high dimensionality,making downstream tasks such as visualization more difficult. We appliedPoincar\'e embeddings in a 2-dimensional hyperbolic space to a large-scaleadministrative claims database and show performance comparable to100-dimensional embeddings in a euclidean space. We then examine diseaserelationships under different disease contexts to better understand potentialphenotypes.

Learning Contextual Hierarchical Structure of Medical Concepts with Poincair\'e Embeddings to Clarify Phenotypes