Multi-Task Pharmacovigilance Mining from Social Media Posts

TL;DR

This paper introduces a multi-task neural network framework for pharmacovigilance that simultaneously classifies adverse drug reaction posts, extracts ADR mentions, and identifies drug indications from social media data, improving monitoring accuracy.

Contribution

The novel multi-task neural network effectively combines multiple pharmacovigilance tasks with limited data, utilizing a coverage-based attention mechanism for better identification of multi-word ADRs and indications.

Findings

Outperforms state-of-the-art models on Twitter datasets

Accurately classifies ADR posts and extracts ADR mentions

Effectively identifies drug indications from social media

Abstract

Social media has grown to be a crucial information source for pharmacovigilance studies where an increasing number of people post adverse reactions to medical drugs that are previously unreported. Aiming to effectively monitor various aspects of Adverse Drug Reactions (ADRs) from diversely expressed social medical posts, we propose a multi-task neural network framework that learns several tasks associated with ADR monitoring with different levels of supervisions collectively. Besides being able to correctly classify ADR posts and accurately extract ADR mentions from online posts, the proposed framework is also able to further understand reasons for which the drug is being taken, known as 'indication', from the given social media post. A coverage-based attention mechanism is adopted in our framework to help the model properly identify 'phrasal' ADRs and Indications that are attentive to multiple words in a post. Our framework is applicable in situations where limited parallel data for different pharmacovigilance tasks are available.We evaluate the proposed framework on real-world Twitter datasets, where the proposed model outperforms the state-of-the-art alternatives of each individual task consistently.