| アブストラクト | BACKGROUND: 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. OBJECTIVE: This study aims to monitor AEs shared in social media and online support groups using medical context-aware natural language processing language models. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. |
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| ジャーナル名 | JMIR infodemiology |
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| Pubmed追加日 | 2024/12/20 |
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| 投稿者 | Daluwatte, Chathuri; Khromava, Alena; Chen, Yuning; Serradell, Laurence; Chabanon, Anne-Laure; Chan-Ou-Teung, Anthony; Molony, Cliona; Juhaeri, Juhaeri |
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| 組織名 | Digital Data, Sanofi, Cambridge, MA, United States.;Epidemiology and Benefit-Risk Department, Sanofi, Toronto, ON, Canada.;Epidemiology and Benefit-Risk Department, Sanofi, Lyon, France.;Global Pharmacovigilance Department, Sanofi, Lyon, France.;Digital Data, Sanofi, Lyon, France.;Epidemiology and Benefit-Risk Department, Sanofi, Bridgewater, NJ, United States. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/39705077/ |
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