  In this work we address the issue of generic automated disease incidence
monitoring on twitter. We employ an ontology of disease related concepts and
use it to obtain a conceptual representation of tweets. Unlike previous key
word based systems and topic modeling approaches, our ontological approach
allows us to apply more stringent criteria for determining which messages are
relevant such as spatial and temporal characteristics whilst giving a stronger
guarantee that the resulting models will perform well on new data that may be
lexically divergent. We achieve this by training learners on concepts rather
than individual words. For training we use a dataset containing mentions of
influenza and Listeria and use the learned models to classify datasets
containing mentions of an arbitrary selection of other diseases. We show that
our ontological approach achieves good performance on this task using a variety
of Natural Language Processing Techniques. We also show that word vectors can
be learned directly from our concepts to achieve even better results.
