Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF

Buzhou Tang,Xiaolong Wang,Qingcai Chen,Jianglu Hu

doi:10.1155/2018/2379208

Buzhou Tang, Xiaolong Wang + Show 2 more

Open Access

https://doi.org/10.1155/2018/2379208

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Abstract

Social media in medicine, where patients can express their personal treatment experiences by personal computers and mobile devices, usually contains plenty of useful medical information, such as adverse drug reactions (ADRs); mining this useful medical information from social media has attracted more and more attention from researchers. In this study, we propose a deep neural network (called LSTM-CRF) combining long short-term memory (LSTM) neural networks (a type of recurrent neural networks) and conditional random fields (CRFs) to recognize ADR mentions from social media in medicine and investigate the effects of three factors on ADR mention recognition. The three factors are as follows: (1) representation for continuous and discontinuous ADR mentions: two novel representations, that is, “BIOHD” and “Multilabel,” are compared; (2) subject of posts: each post has a subject (i.e., drug here); and (3) external knowledge bases. Experiments conducted on a benchmark corpus, that is, CADEC, show that LSTM-CRF achieves better F-score than CRF; “Multilabel” is better in representing continuous and discontinuous ADR mentions than “BIOHD”; both subjects of comments and external knowledge bases are individually beneficial to ADR mention recognition. To the best of our knowledge, this is the first time to investigate deep neural networks to mine continuous and discontinuous ADRs from social media.

Highlights

With rapid growth of online health social networks, such as DailyStrength.com [1], Askapatient.com [2], MedHelp.org [3], and PatientsLikeMe.com [4], more and more patients share their personal health-related information through social media posts
We propose a deep neural network, which combines long shortterm memory (LSTM) neural networks and conditional random fields (CRFs), to recognize both continuous and discontinuous adverse drug reactions (ADRs) mentions from social media
Our results show that long short-term memory (LSTM)-CRF performs better than CRF, “Multilabel” is more suitable than “BIOHD” to represent continuous and discontinuous ADR mentions, and both subject of posts and external knowledge bases are individually beneficial to ADR mention recognition

Summary

Introduction

With rapid growth of online health social networks, such as DailyStrength.com [1], Askapatient.com [2], MedHelp.org [3], and PatientsLikeMe.com [4], more and more patients share their personal health-related information through social media posts. This information can be utilized for public health monitoring, for pharmacovigilance via mining adverse drug reactions (ADRs) using natural language processing techniques. . .legs,” and “stiffness,” where “pain in arms” and “stiffness” are continuous ADR mentions composed of continuous words and “pain in. Given a user’s post “I still have pain in arms and legs with much stiffness.” our goal is to extract three ADR mentions, namely, “pain in arms,” “pain in. . .legs,” and “stiffness,” where “pain in arms” and “stiffness” are continuous ADR mentions composed of continuous words and “pain in. . .legs” is a discontinuous ADR mention composed of discontinuous words

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