Reply to comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".
Journal
Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
Titre abrégé: J Am Med Inform Assoc
Pays: England
ID NLM: 9430800
Informations de publication
Date de publication:
01 06 2019
01 06 2019
Historique:
received:
19
12
2018
accepted:
22
12
2018
pubmed:
14
4
2019
medline:
29
12
2020
entrez:
14
4
2019
Statut:
ppublish
Résumé
We appreciate the detailed review provided by Magge et al1 of our article, "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts." 2 In their letter, they present a subjective criticism that rests on concerns about our dataset composition and potential misinterpretation of comparisons to existing methods. Our article underwent two rounds of extensive peer review and has been cited 28 times1 in the nearly 2 years since it was published online (February 2017). Neither the reviewers nor the citing authors raised similar concerns. There are, however, portions of the commentary that highlight areas of our work that would benefit from further clarification.
Identifiants
pubmed: 30980667
pii: 5453996
doi: 10.1093/jamia/ocy192
pmc: PMC7647328
doi:
Types de publication
Letter
Comment
Langues
eng
Sous-ensembles de citation
IM
Pagination
580-581Commentaires et corrections
Type : CommentOn
Informations de copyright
© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Références
J Am Med Inform Assoc. 2011 Sep-Oct;18(5):540-3
pubmed: 21846785
J Am Med Inform Assoc. 2015 May;22(3):671-81
pubmed: 25755127
J Am Med Inform Assoc. 2017 Jul 1;24(4):813-821
pubmed: 28339747
J Am Med Inform Assoc. 2019 Jun 1;26(6):577-579
pubmed: 31087070