@文章{信息:doi/10.2196/33792，作者="Klein, Ari Z和O'Connor, Karen和Gonzalez-Hernandez, Graciela"，标题="使用Twitter数据监测怀孕期间COVID-19疫苗安全性:队列识别的概念验证研究"，期刊="JMIR Form Res"，年="2022"，月="Jan"，日="6"，卷="6"，数="1"，页="e33792"，关键词="自然语言处理;社交媒体;COVID-19;数据挖掘;COVID-19疫苗;妊娠结局”，摘要=“背景:怀孕期间COVID-19与孕产妇死亡、重症监护病房住院和早产风险增加有关;然而，由于缺乏安全数据，许多孕妇拒绝接种COVID-19疫苗。目的:这项初步研究的目的是评估Twitter数据是否可用于确定怀孕期间COVID-19疫苗接种流行病学研究的队列。具体来说，我们研究了是否有可能识别那些报告(1)在怀孕期间或受孕围期接种了COVID-19疫苗的用户，以及(2)他们的怀孕结果。方法:我们开发了正则表达式，在2021年7月初之前在推特上宣布怀孕的用户发布的大量推文中搜索COVID-19疫苗接种的报告。 To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes. Results: We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75{\%}) completed pregnancies. Conclusions: Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies. ", issn="2561-326X", doi="10.2196/33792", url="https://formative.www.mybigtv.com/2022/1/e33792", url="https://doi.org/10.2196/33792", url="http://www.ncbi.nlm.nih.gov/pubmed/34870607" }