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

The Healthy States of America: Creating a Health Taxonomy with Social Media