Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
J Am Med Inform Assoc. 2019 Jun 1;26(6):580-581. doi: 10.1093/jamia/ocy192.
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.
我们感谢 Magge 等人 1 对我们的文章“用于药物警戒的深度学习：用于在 Twitter 帖子中标记不良反应的循环神经网络架构”2 的详细评论。在他们的信中，他们提出了一个主观的批评，主要是基于对我们数据集组成的担忧以及对与现有方法进行比较的潜在误解。我们的文章经过两轮广泛的同行评审，自在线发表以来（2017 年 2 月）近 2 年来已被引用 28 次 1。评论者和引用作者都没有提出类似的担忧。但是，评论中有一些部分突出了我们的工作需要进一步澄清的方面。
