Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets

TL;DR

This paper introduces a multi-task learning approach for extracting adverse drug reaction mentions from tweets, leveraging auxiliary tasks and weak supervision to improve accuracy over existing methods.

Contribution

It proposes a novel multi-task learning framework with automatic weak supervision generation for ADR extraction from social media data.

Findings

Outperforms state-of-the-art methods by 7.2% F1 score.

Utilizes auxiliary adverse drug event detection to enhance ADR extraction.

Effectively leverages large unlabeled tweet datasets for weak supervision.

Abstract

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.