Social media has been considered as a data source for track- ing disease. However, most analyses are based on models that prioritize strong correlation with population-level dis- ease rates over determining whether or not specific individ- ual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detec- Tion at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individ- ual's publicly available Twitter data. We find that about half (17=35 = 48:57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By develop- ing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diag- nose an individual with greater than 99% accuracy even if she does not discuss her health.