| アブストラクト | 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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| ジャーナル名 | International journal of molecular sciences |
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| 投稿日 | 2022/7/29 |
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| 投稿者 | Flora, James; Khan, Wasiq; Jin, Jennifer; Jin, Daniel; Hussain, Abir; Dajani, Khalil; Khan, Bilal |
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| 組織名 | Department of Computer Science and Engineering, California State University San;Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA.;School of Computer Science and Mathematics, Liverpool John Moores University,;Liverpool L3 3AF, UK.;Division of Vascular & Interventional Radiology, Department of Radiology, Loma;Linda University Medical Center, Loma Linda, CA 92354, USA.;Department of Electrical Engineering, University of Sharjah, Sharjah P.O. Box;27272, United Arab Emirates.;Institute of the Environment and Sustainability, University of California Los;Angeles, Los Angeles, CA 90095, USA. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/35897804/ |
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