Department of Data-Centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea.
Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea.
PLoS One. 2023 Feb 21;18(2):e0282119. doi: 10.1371/journal.pone.0282119. eCollection 2023.
After the COVID-19 pandemic, the world has made efforts to recover from the chaotic situation. Vaccination is a way to help control infectious diseases, and many people have been vaccinated against COVID-19 by this point. However, an extremely small number of those who received the vaccine have experienced diverse side effects.
In this study, we examined people who experienced adverse events with the COVID-19 vaccine by gender, age, vaccine manufacturer, and dose of vaccinations by using the Vaccine Adverse Event Reporting System datasets. Then we used a language model to vectorize symptom words and reduced their dimensionality. We also clustered symptoms by using unsupervised machine learning and analyzed the characteristics of each symptom cluster. Lastly, to discover any association rules among adverse events, we used a data mining approach. The frequency of adverse events was higher for women than men, for Moderna than for Pfizer or Janssen, and for the first dose than for the second dose. However, we found that characteristics of vaccine adverse events, including gender, vaccine manufacturer, age, and underlying diseases were different for each symptom cluster, and that fatal cases were significantly related to a particular cluster (associated with hypoxia). Also, as a result of the association analysis, the {chills ↔ pyrexia} and {vaccination site pruritus ↔ vaccination site erythema} rules had the highest support value of 0.087 and 0.046, respectively.
We aim to contribute accurate information on the adverse events of the COVID-19 vaccine to relieve public anxiety due to unconfirmed statements about vaccines.
新冠疫情后，全球为摆脱混乱局面付出了诸多努力。接种疫苗是帮助控制传染病的一种方式，目前已有许多人接种了新冠疫苗。然而，极少数人在接种疫苗后出现了各种不良反应。
本研究利用疫苗不良事件报告系统数据集，按性别、年龄、疫苗制造商和接种剂量，调查了发生新冠疫苗不良事件的人群。然后，我们使用语言模型对症状词进行向量化并降维。我们还使用无监督机器学习对症状进行聚类，并分析每个症状簇的特征。最后，为了发现不良事件之间的关联规则，我们使用了数据挖掘方法。女性不良事件的频率高于男性，莫德纳（Moderna）高于辉瑞（Pfizer）或杨森（Janssen），第一剂高于第二剂。但是，我们发现，每个症状簇的疫苗不良事件特征，包括性别、疫苗制造商、年龄和基础疾病均不同，且缺氧相关症状簇与死亡病例显著相关。此外，通过关联分析，{寒战↔发热}和{接种部位瘙痒↔接种部位红斑}规则的支持度最高，分别为 0.087 和 0.046。
我们旨在为新冠疫苗的不良事件提供准确信息，以缓解公众对未经证实的疫苗相关言论的焦虑。
