Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets
This addresses the need for faster adverse drug reaction monitoring in healthcare by leveraging social media, though it is incremental as it builds on existing neural network approaches.
The paper tackled the problem of extracting adverse drug reaction mentions from tweets to improve real-time surveillance, proposing a multi-task learning method that outperformed state-of-the-art methods by 7.2% in F1 score using 0.48M 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 in ADR mention extraction uses Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task, i.e., ADR extraction. Furthermore, in the absence of auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with 0.48M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by 7.2% in terms of F1 score.