Named Entity Recognition in Pubmed Abstracts for Pharmacovigilance Using Deep Learning

Abstract

Methods of natural language processing associated with machine learning or deep learning can support detection of adverse drug reactions in abstracts of case reports available on Pubmed. In 2012, Gurulingappa et al. proposed a training set for the recognition of named entities corresponding to drugs and adverse reactions on 3000 Pubmed abstracts. We implemented a classifier using deep learning with a Bi-LSTM and a CRF layer that achieves an F-measure of 87.8%. Perspectives consist in using BERT for improving the classifier, and applying it to a large number of Pubmed abstract to build a database of case reports available in the literature.