1. A computer-implemented method for detection of latent signals in adverse event information, comprising: receiving a set of drug and event information that includes a first set of adverse event information and further includes prescription and morbidity information; identifying a second set of events associated with the first set of adverse events; computing covariances in drug co-prescription from the set of drug and event information; computing covariances in co-morbities from the set of drug and event information; approximating adverse event biases based on the covariances in drug co-prescription and comorbidities; applying a statistical analysis to the second set of events to determine a subset of the second set of events that occurs above a predetermined level with the first set of adverse events, wherein the statistical analysis is corrected based on the approximated adverse event biases; receiving a training dataset that includes drug and event information; training a predictive model using the subset of the second set of events and the training dataset, wherein the predictive model is trained to detect a detected set of adverse events; and applying the predictive model to a test dataset to determine the detected set of adverse events.