Detecting Adverse Drug Reaction (ADR) Mentions from Social Media

Abstract

Adverse Drug Reaction (ADR) can be described as undesired consequences
resulting from consumption of medical prescribed drugs. Such reactions are often missed
in clinical trials and are experienced by real world patients. If reported and corrected at
early stages, it could be beneficial for other patients having the same disease. Many
government agencies across different countries are aiming to collect the real time drug
reactions from patients through surveys, reporting tools etc. The direct feedback from
patients based on their experience is more relevant and informative in making decisions on
restricting adverse drug reactions. This self-reported patient feedback in free flowing
format is available from discussions in social media and medical blogs. Such data have
more impact than government induced feedback, reporting and surveys. In this paper our
primary focus is to utilize social media to mine the ADR mentions from patients. We filter
tweets that might have adverse drug reaction mentions. We compare the performance of
various classification algorithms in classifying accurately if a tweet comprises of ADR
mention or not.