Evaluation of text-processing algorithms for adverse drug event extraction from social media

Abstract

The discovery of suspected adverse drug reactions is no longer restricted to mining reports that pharmaceutical companies and health professionals send to regulators for possible safety signals. Patient forums and other social media are being studied for additional sources of information to assist in expediting adverse reaction discovery. Extracting information on drugs, adverse drug reactions, diseases and symptoms, or patient demographics from such media is an essential step of this process, but it is not straightforward. While most studies in this area use a lexicon-based information extraction methodology, they do not explicitly evaluate the impact of text-processing steps on their final results. We experimentally quantify the value of the most popular techniques to establish whether or not they benefit the information extraction process.