语言模型工具的应用,用于COVID-19疫苗不良事件监测,使用网络和社交媒体内容:算法开发和验证研究
Chathuri Daluwatte1, Alena Khromava2, Yuning Chen1
1Digital Data, Sanofi, Cambridge, MA, United States.
JMIR infodemiology
|December 20, 2024
概括
社交媒体监控可以比传统系统更快地检测与COVID-19疫苗相关的不良事件 (AE). 这种方法增强了疫苗安全监督和公众信心.
科学领域:
- 药物监督和公共卫生监督
- 计算语言学和自然语言处理
- 疫苗安全研究 疫苗安全研究
背景情况:
- 传统的疫苗不良事件自发报告系统有很大的时间滞后.
- 全球COVID-19疫苗接种活动需要实时监测潜在的AE.
- 社交媒体和网络内容为更快的AE检测提供了机会.
研究的目的:
- 使用社交媒体和在线支持小组监测与COVID-19疫苗相关的不良事件 (AE).
- 为了利用医疗上下文感知自然语言处理 (NLP) 语言模型来检测AE.
- 通过实时社交媒体洞察来增强传统的药监数据.
主要方法:
- 开发了一个网络应用程序,分析全球社交媒体,博客和论坛的61种语言的COVID-19疫苗关键字.
- 使用基于PubmedBERT的命名实体识别模型来识别普通语言AE (精度=0.76,回忆=0.82).
- 使用知识图表将已识别的AE映射到监管活动医学词典 (MedDRA) 术语中,并通过视觉分析呈现了发现.
主要成果:
- 大多数检测到的AE发生在2021年,在2022年减少.
- 通过网络应用程序识别的副作用与卫生当局在相似时期报告的副作用一致.
- 该系统与已建立的安全报告道一致.
结论:
- 网络和社交媒体监控可以促进早期检测潜在的疫苗相关不良事件 (AE).
- 这种方法通过提供用于信号检测的补充数据源来增强营销后监测.
- 利用社交媒体数据可以改善与利益相关者的沟通,并增强对疫苗安全监测系统的信心.
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