Detecting Adverse Drug Reactions on Twitter with Convolutional Neural Networks and Word Embedding Features

Author:

Masino Aaron J.ORCID,Forsyth Daniel,Fiks Alexander G.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Science Applications,Health Informatics,Information Systems

Reference33 articles.

1. Leaman R, Wojtulewicz L, Sullivan R, Skariah A, Yang J, Gonzalez G (2010) Towards Internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In: Proc. 2010 Work. Biomed. Nat. Lang. Process., Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 117–125. http://dl.acm.org/citation.cfm?id=1869961.1869976

2. Benton A, Ungar L, Hill S, Hennessy S, Mao J, Chung A, Leonard CE, Holmes JH (2011) Identifying potential adverse effects using the web: a new approach to medical hypothesis generation. J Biomed Inform 44:989–996. https://doi.org/10.1016/j.jbi.2011.07.005

3. Yang CC, Jiang L, Yang H, Tang X (2012) Detecting signals of adverse drug reactions from health consumer contributed content in social media. Proc ACM SIGKDD Work Heal Informatics

4. Yates A, Goharian N (2013) ADRTrace: detecting expected and unexpected adverse drug reactions from user reviews on social media sites. In: Serdyukov P, Braslavski P, Kuznetsov S, Kamps J, Rüger S, Agichtein E, Segalovich I, Yilmaz E (Eds) Adv. Inf. Retr. SE - 92, Springer Berlin Heidelberg, pp 816–819. doi: https://doi.org/10.1007/978-3-642-36973-5_92.

5. White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E (2013) Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Inform Assoc 20:404–408. https://doi.org/10.1136/amiajnl-2012-001482

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