  Traditional disease surveillance can be augmented with a wide variety of
real-time sources such as, news and social media. However, these sources are in
general unstructured and, construction of surveillance tools such as
taxonomical correlations and trace mapping involves considerable human
supervision. In this paper, we motivate a disease vocabulary driven word2vec
model (Dis2Vec) to model diseases and constituent attributes as word embeddings
from the HealthMap news corpus. We use these word embeddings to automatically
create disease taxonomies and evaluate our model against corresponding human
annotated taxonomies. We compare our model accuracies against several
state-of-the art word2vec methods. Our results demonstrate that Dis2Vec
outperforms traditional distributed vector representations in its ability to
faithfully capture taxonomical attributes across different class of diseases
such as endemic, emerging and rare.
