Evidence map›Paper›PMID 40635461›Full record

ArticleEpidemiology and health2025

Identifying adverse reactions following COVID-19 vaccination in Korea using data from active surveillance: a text mining approach.

Hye Ah Lee, Bomi Park, Chung Ho Kim, Yeonjae Kim, Hyunjin Park, Seunghee Jun, Hyelim Lee, Seunghyun Lewis Kwon, Yesul Heo, Hyungmin Lee and 2 more

Abstract read
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Article in Epidemiology and health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Hye Ah LeeClinical Trial Center, Ewha Womans University Mokdong Hospital, Seoul, Korea.
Bomi ParkCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Chung Ho KimCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Yeonjae KimCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Hyunjin ParkCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Seunghee JunCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Hyelim LeeCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
Seunghyun Lewis KwonDivision of Immunization Services, Korea Disease Control and Prevention Agency, Cheongju, Korea.
Yesul HeoDivision of Immunization Policy, Korea Disease Control and Prevention Agency, Cheongju, Korea.
Hyungmin LeeDivision of Immunization Policy, Korea Disease Control and Prevention Agency, Cheongju, Korea.
Hyesook ParkCOVID-1 9 Vaccine Safety Research Center, Seoul, Korea.
COVID-19 Vaccine Safety Research Committee

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesUnstructured 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.

methodsWe 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Adverse Drug Reaction Reporting SystemsCOVID-19COVID-19 VaccinesData MiningVaccinationAdolescentAdultAgedChildChild, PreschoolFemaleHumansMaleMiddle AgedRepublic of KoreaYoung AdultCOVID-19 VaccinesAdverse reactionsData miningPharmacovigilanceSafetyVaccination

Identifiers

PMID40635461
PMCPMC12425858

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.