Lee Hye Ah, Park Bomi, Kim Chung Ho, Kim Yeonjae, Park Hyunjin, Jun Seunghee, Lee Hyelim, Kwon Seunghyun Lewis, Heo Yesul, Lee Hyungmin, Park Hyesook
Clinical Trial Center, Ewha Womans University Mokdong Hospital, Seoul, Korea.
COVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Epidemiol Health. 2025;47:e2025034. doi: 10.4178/epih.e2025034. Epub 2025 Jun 30.
Unstructured text data collected through vaccine safety surveillance systems can identify previously unreported adverse reactions and provide critical information to enhance these systems. This study explored adverse reactions using text data collected through an active surveillance system following coronavirus disease 2019 (COVID-19) vaccination.
We performed text mining on 2,608 and 2,054 records from 2 survey seasons (2023-2024 and 2024-2025), in which participants reported health conditions experienced within 7 days of vaccination using free-text responses. Frequency analysis was conducted to identify key terms, followed by subgroup analyses by sex, age, and concomitant influenza vaccination. In addition, semantic network analysis was used to examine terms reported together.
The analysis identified several common (≥1%) adverse events, such as respiratory symptoms, sleep disturbances, lumbago, and indigestion, which had not been frequently noted in prior literature. Moreover, less frequent (≥0.1 to <1.0%) adverse reactions affecting the eyes, ears, and oral cavity were also detected. These adverse reactions did not differ significantly in frequency based on the presence or absence of simultaneous influenza vaccination. Co-occurrence analysis and estimation of correlation coefficients further revealed associations between frequently co-reported symptoms.
This study utilized text mining to uncover previously unrecognized adverse reactions associated with COVID-19 vaccination, thereby broadening our understanding of the vaccine's safety profile. The insights obtained may inform future investigations into vaccine-related adverse reactions and improve the processing of text data in surveillance systems.
通过疫苗安全监测系统收集的非结构化文本数据可识别先前未报告的不良反应，并为加强这些系统提供关键信息。本研究利用通过2019冠状病毒病（COVID-19）疫苗接种后主动监测系统收集的文本数据，探索不良反应情况。
我们对来自2个调查季节（2023 - 2024年和2024 - 2025年）的2608条和2054条记录进行了文本挖掘，在这些记录中，参与者使用自由文本回复报告了接种疫苗后7天内经历的健康状况。进行频率分析以识别关键术语，随后按性别、年龄和同时接种流感疫苗情况进行亚组分析。此外，使用语义网络分析来检查共同报告的术语。
分析确定了几种常见（≥1%）的不良事件，如呼吸道症状、睡眠障碍、腰痛和消化不良，这些在先前的文献中并不常见。此外，还检测到影响眼睛、耳朵和口腔的频率较低（≥0.1%至<1.0%）的不良反应。这些不良反应在是否同时接种流感疫苗方面，频率没有显著差异。共现分析和相关系数估计进一步揭示了频繁共同报告症状之间的关联。
本研究利用文本挖掘揭示了与COVID-19疫苗接种相关的先前未被认识的不良反应，从而拓宽了我们对疫苗安全性概况的理解。所获得的见解可能为未来对疫苗相关不良反应的调查提供信息，并改善监测系统中文本数据的处理。
