Daluwatte Chathuri, Khromava Alena, Chen Yuning, Serradell Laurence, Chabanon Anne-Laure, Chan-Ou-Teung Anthony, Molony Cliona, Juhaeri Juhaeri
Digital Data, Sanofi, Cambridge, MA, United States.
Epidemiology and Benefit-Risk Department, Sanofi, Toronto, ON, Canada.
JMIR Infodemiology. 2024 Dec 20;4:e53424. doi: 10.2196/53424.
Spontaneous pharmacovigilance reporting systems are the main data source for signal detection for vaccines. However, there is a large time lag between the occurrence of an adverse event (AE) and the availability for analysis. With global mass COVID-19 vaccination campaigns, social media, and web content, there is an opportunity for real-time, faster monitoring of AEs potentially related to COVID-19 vaccine use. Our work aims to detect AEs from social media to augment those from spontaneous reporting systems.
This study aims to monitor AEs shared in social media and online support groups using medical context-aware natural language processing language models.
We developed a language model-based web app to analyze social media, patient blogs, and forums (from 190 countries in 61 languages) around COVID-19 vaccine-related keywords. Following machine translation to English, lay language safety terms (ie, AEs) were observed using the PubmedBERT-based named-entity recognition model (precision=0.76 and recall=0.82) and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms using knowledge graphs (MedDRA terminology is an internationally used set of terms relating to medical conditions, medicines, and medical devices that are developed and registered under the auspices of the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use). Weekly and cumulative aggregated AE counts, proportions, and ratios were displayed via visual analytics, such as word clouds.
Most AEs were identified in 2021, with fewer in 2022. AEs observed using the web app were consistent with AEs communicated by health authorities shortly before or within the same period.
Monitoring the web and social media provides opportunities to observe AEs that may be related to the use of COVID-19 vaccines. The presented analysis demonstrates the ability to use web content and social media as a data source that could contribute to the early observation of AEs and enhance postmarketing surveillance. It could help to adjust signal detection strategies and communication with external stakeholders, contributing to increased confidence in vaccine safety monitoring.
自发药物警戒报告系统是疫苗信号检测的主要数据源。然而，不良事件（AE）发生与可供分析之间存在很大的时间滞后。随着全球大规模新冠疫苗接种运动、社交媒体和网络内容的出现，有机会对可能与新冠疫苗使用相关的不良事件进行实时、更快的监测。我们的工作旨在从社交媒体中检测不良事件，以补充自发报告系统中的不良事件。
本研究旨在使用医学上下文感知自然语言处理语言模型监测社交媒体和在线支持小组中分享的不良事件。
我们开发了一个基于语言模型的网络应用程序，以分析围绕新冠疫苗相关关键词的社交媒体、患者博客和论坛（来自61种语言的190个国家）。在机器翻译为英语后，使用基于PubmedBERT的命名实体识别模型（精确率=0.76，召回率=0.82）观察通俗语言安全术语（即不良事件），并使用知识图谱将其映射到《药物监管活动医学词典》（MedDRA）术语（MedDRA术语是一套国际通用的与医疗状况、药物和医疗器械相关的术语，由国际人用药品技术协调理事会主持制定和注册）。通过词云等视觉分析展示每周和累计汇总的不良事件计数、比例和比率。
大多数不良事件在2021年被识别，2022年较少。使用网络应用程序观察到的不良事件与卫生当局在不久前或同期通报的不良事件一致。
监测网络和社交媒体提供了观察可能与新冠疫苗使用相关的不良事件的机会。所呈现的分析表明，能够将网络内容和社交媒体用作数据源，这有助于不良事件的早期观察并加强上市后监测。它有助于调整信号检测策略以及与外部利益相关者的沟通，增强对疫苗安全监测的信心。
