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Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.

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  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel, Switzerland : MDPI, [2000-
    • Subject Terms:
      COVID-19*/epidemiology ; COVID-19*/prevention & control ; COVID-19 Vaccines*/adverse effects; Adverse Drug Reaction Reporting Systems ; Child ; Female ; Humans ; Machine Learning ; Male ; Pain/chemically induced ; Penicillins ; United States ; Vaccines/adverse effects
    • Abstract:
      Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).
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    • Contributed Indexing:
      Keywords: COVID-19; VAERS; adverse events; association rule mining; bipartite graphs; hierarchical clustering; self-organizing maps; vaccine analysis workflow; vaccine development
    • Accession Number:
      0 (COVID-19 Vaccines)
      0 (Penicillins)
      0 (Vaccines)
    • Publication Date:
      Date Created: 20220728 Date Completed: 20220729 Latest Revision: 20251031
    • Publication Date:
      20251031
    • Accession Number:
      PMC9368306
    • Accession Number:
      10.3390/ijms23158235
    • Accession Number:
      35897804