  Since the uptake of social media, researchers have mined online discussions
to track the outbreak and evolution of specific diseases or chronic conditions
such as influenza or depression. To broaden the set of diseases under study, we
developed a Deep Learning tool for Natural Language Processing that extracts
mentions of virtually any medical condition or disease from unstructured social
media text. With that tool at hand, we processed Reddit and Twitter posts,
analyzed the clusters of the two resulting co-occurrence networks of
conditions, and discovered that they correspond to well-defined categories of
medical conditions. This resulted in the creation of the first comprehensive
taxonomy of medical conditions automatically derived from online discussions.
We validated the structure of our taxonomy against the official International
Statistical Classification of Diseases and Related Health Problems (ICD-11),
finding matches of our clusters with 20 official categories, out of 22. Based
on the mentions of our taxonomy's sub-categories on Reddit posts geo-referenced
in the U.S., we were then able to compute disease-specific health scores. As
opposed to counts of disease mentions or counts with no knowledge of our
taxonomy's structure, we found that our disease-specific health scores are
causally linked with the officially reported prevalence of 18 conditions.
