Reply to comment on: “Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts”
Author(s) -
Anne Cocos,
Alexander G. Fiks,
Aaron J. Masino
Publication year - 2018
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocy192
Subject(s) - pharmacovigilance , criticism , computer science , drug reaction , deep learning , artificial intelligence , adverse drug reaction , data science , drug , medicine , psychiatry , political science , law
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.
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