Volume, complexity, and time constraints of adverse event reporting are overwhelming the pharmacovigilance workforce. New solutions are needed to support these activities to meet global regulatory timelines.

We developed several augmented intelligence approaches to support the correct identification and classification of seriousness, a key factor in adverse reporting, in various document types.

Our deep learning models were trained using an extensive data set that captured deep institutional pharmacovigilance practitioner knowledge.