Figure 1.

Methodological overview of latent signal detection algorithm. (a) Learning the side-effect profile of a disease. We assessed each adverse event reported in the Adverse Event Reporting System, S_i, for significant enrichment on reports involving a type 2 diabetes mellitus (T2DM) drug (identified by the triangle indicators) vs. a background of all drugs and determined the significance by using a Fisher’s exact test. We call the result of this analysis the “disease’s side-effect profile.” (b) We then extracted reports for all possible pairs of drugs (~40,000) and scored each drug pair’s side-effect profile for its similarity to the disease’s profile. Note that we considered only the adverse events that were significantly correlated with the disease (colored boxes, i.e., S₁, S₂, and S₃) and not others (gray boxes). We ranked each drug pair according to its similarity score, and (c) clinically validated the top-ranked pairs of drugs for interaction effects on a predetermined phenotype extracted from the electronic medical records (EMRs) (e.g., random blood glucose concentration). Dx, diagnosis.