Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

TL;DR

This paper introduces a semi-supervised RNN model for extracting adverse drug reaction mentions from social media, effectively utilizing unlabeled data to overcome the scarcity of annotated datasets and achieving state-of-the-art results.

Contribution

A novel semi-supervised RNN approach that leverages unlabeled social media data for improved ADR mention extraction performance.

Findings

Achieved state-of-the-art ADR mention extraction accuracy.

Effectively utilized unlabeled social media data.

Outperformed existing supervised methods.

Abstract

Social media is an useful platform to share health-related information due to its vast reach. This makes it a good candidate for public-health monitoring tasks, specifically for pharmacovigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from twitter. Medical information extraction from social media is challenging, mainly due to short and highly information nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are Long-Short-Term-Memory networks (LSTMs) \cite{hochreiter1997long}. Deep neural networks, due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in real-world, it is hard to get large labeled data, mainly due to heavy cost associated with manual annotation. Towards this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. Through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.