Identifying Adverse Drug Reactions by analyzing Twitter messages

Adverse drug reactions (ADRs) have become the most common cause of deaths in the world despite post marketing drug surveillance. Expensive clinical trials do not uncover all the ADRs and also cumbersome for consumers and healthcare professionals. Majority of existing methods rely on patients' spontaneous self-reports. The recent explosion of micro blogging platforms such as Twitter presents a new information source to discover ADRs. In this study, the authors developed a system to automatically extract ADRs from Twitter messages utilizing Natural Language Processing (NLP) techniques. First, the authors proposed a novel method to filter out all the drug related messages from the Twitter data stream. Dictionary based approaches were used to identify medical terminology, emoticons and slang words. The interpretation of “internet language” was also addressed in this research. The best classifier for the classification of ADR reached an accuracy of 68% with F-measure of 69%. The results suggest that there is potential for extracting ADR related information from Twitter messages to support pharmacovigilance.