Department of Computer Information Technology & Graphics, Purdue University Northwest, Hammond, Indiana, U.S.A.
School of Information and Intelligent Engineering, Ningbo City College of Vocational Technology, Ningbo, Zhejiang, China.
Stud Health Technol Inform. 2022 Jun 6;290:767-771. doi: 10.3233/SHTI220182.
Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.
最近，药物警戒学领域的一个活跃研究方向是利用 Twitter 等社交媒体作为替代数据源，收集与药物使用相关的患者生成信息。已发表的大多数工作都集中在识别社交媒体数据中不良反应的提及，但很少调查提到的药物与任何提到的效果表达之间的关系。在这项研究中，我们将这种关系提取任务视为分类问题，并使用神经嵌入来表示 Twitter 文本，然后将其输入到循环神经网络分类器中。我们的方法的分类性能在 9516 条带注释的推文语料库上与 4 种基线词嵌入方法进行了比较。
