Feature Engineering for Recognizing Adverse Drug Reactions from Twitter Posts

Social media platforms are emerging digital communication channels that provide an easy way for common people to share their health and medication experiences online. With more people discussing their health information online publicly, social media platforms present a rich source of information for exploring adverse drug reactions (ADRs). ADRs are major public health problems that result in deaths and hospitalizations of millions of people. Unfortunately, not all ADRs are identified before a drug is made available in the market. In this study, an ADR event monitoring system is developed which can recognize ADR mentions from a tweet and classify its assertion. We explored several entity recognition features, feature conjunctions, and feature selection and analyzed their characteristics and impacts on the recognition of ADRs, which have never been studied previously. The results demonstrate that the entity recognition performance for ADR can achieve an F-score of 0.562 on the PSB Social Media Mining shared task dataset, which outperforms the partial-matching-based method by 0.122. After feature selection, the F-score can be further improved by 0.026. This novel technique of text mining utilizing shared online social media data will open an array of opportunities for researchers to explore various health related issues.