MULTI-TASK LEARNING IN PHARMACOVIGILANCE

摘要

Techniques for pharmacovigilence adverse-event processing include receiving data comprising medical narrative text and generating, based on the received data, using a recurrent neural network encoder, a fixed-length context vector representation of the medical narrative text. The fixed-length context vector representation may then be queried, using a recurrent neural network decoder, to generate one or more hidden states. A first set of the one or more hidden states may be processed to generate an assessment of seriousness represented by the medical narrative text, and a second set of the one or more hidden states may be processed to generate a plurality of respective assessments of whether respective individual words of the medical narrative text correspond to one or more adverse events.