Signal detection algorithms to identify drug effects and drug interactions

摘要

An algorithm according to an embodiment of the present invention provides for latent signal detection of adverse events. Embodiments infer the presence of adverse drug events from large observational databases housed by the FDA, WHO, and other governmental organizations. The disclosed algorithms do not require the adverse event to be reported explicitly. Instead, the algorithms infer the presence of adverse events through more common secondary effects. In an embodiment, machine learning techniques are used for this purpose.

主权项

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.