Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF
Joint Authors
Wang, Xiaolong
Hu, Jianglu
Chen, Qingcai
Tang, Buzhou
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Tang, Buzhou& Hu, Jianglu& Wang, Xiaolong& Chen, Qingcai. 2018. Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF. Wireless Communications and Mobile Computing،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1215895
Modern Language Association (MLA)
Tang, Buzhou…[et al.]. Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF. Wireless Communications and Mobile Computing No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1215895
American Medical Association (AMA)
Tang, Buzhou& Hu, Jianglu& Wang, Xiaolong& Chen, Qingcai. Recognizing Continuous and Discontinuous Adverse Drug Reaction Mentions from Social Media Using LSTM-CRF. Wireless Communications and Mobile Computing. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1215895
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215895