Comment on: “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts”
Author:
Affiliation:
1. College of Health Solutions, Arizona State University, Scottsdale, Arizona, USA
2. Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
Funder
National Institutes of Health National Library of Medicine
National Library of Medicine or National Institutes of Health
Publisher
Oxford University Press (OUP)
Subject
Health Informatics
Link
http://academic.oup.com/jamia/article-pdf/26/6/577/34153981/ocz013.pdf
Reference8 articles.
1. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features;Nikfarjam;J Am Med Inform Assoc,2015
2. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches;Galar;IEEE Trans Syst Man Cybern Part C,2012
3. Semi- supervised recurrent neural network for adverse drug reaction mention extraction;Shashank;BMC Bioinformatics,2018
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4. DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter;Journal of the American Medical Informatics Association;2021-07-16
5. A New Era in Pharmacovigilance: Toward Real‐World Data and Digital Monitoring;Clinical Pharmacology & Therapeutics;2021-02-28
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