Co-training for Extraction of Adverse Drug Reaction Mentions from Tweets
This work addresses the need for faster adverse drug reaction monitoring in healthcare by leveraging social media, though it is incremental as it builds on existing semi-supervised techniques.
The paper tackled the problem of extracting adverse drug reaction mentions from tweets to improve real-time surveillance, proposing a semi-supervised co-training method that outperformed state-of-the-art methods by 5% in F1 score on 0.1M tweets.
Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art methods in ADR mention extraction use Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a semi-supervised method based on co-training which can exploit a large pool of unlabeled tweets to augment the limited supervised training data, and as a result enhance the performance. Experiments with 0.1M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 5% in terms of F1 score.