Detecting adverse drug effects using link classification on twitter data

Adverse drug events (ADEs) are among the leading causes of death in the United States. Although many ADEs are detected during pharmaceutical drug development and the FDA approval process, all of the possible reactions cannot be identified during this period. Currently, post-consumer drug surveillance relies on voluntary reporting systems, such as the FDA's Adverse Event Reporting System (AERS). With an increase in availability of medical resources and health related data online, interest in medical data mining has grown rapidly. This information coupled with online conversations of people which involve discussions about their health provide a substantial resource for the identification of ADEs. In this work, we propose a method to identify adverse drug effects from tweets by modeling it as a link classification problem in graphs. Drug and symptom mentions are extracted from the tweet history of each user and a drug-symptom graph is built, where nodes represent either drugs or symptoms and edges are labelled positive or negative, for desired or adverse drug effects respectively. A link classification model is then used to identify negative edges i.e. adverse drug effects. We test our model on 864 users using 10-fold cross validation with Sider's dataset as ground truth. Our model was able to achieve an F-Score of 0.77 compared to the best baseline model with an F-Score of 0.58.