Flora, J, Khan, W, Jin, J, Jin, D, Hussain, A, Dajani, K and Khan, B (2022) Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines. International Journal of Molecular Sciences, 23 (15). p. 8235. ISSN 1661-6596

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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).

Item Type:	Article
Uncontrolled Keywords:	Humans; Pain; Penicillins; Vaccines; Adverse Drug Reaction Reporting Systems; Child; United States; Female; Male; Machine Learning; COVID-19; COVID-19 Vaccines; COVID-19; VAERS; adverse events; association rule mining; bipartite graphs; hierarchical clustering; self-organizing maps; vaccine analysis workflow; vaccine development; Adverse Drug Reaction Reporting Systems; COVID-19; COVID-19 Vaccines; Child; Female; Humans; Machine Learning; Male; Pain; Penicillins; United States; Vaccines; 0399 Other Chemical Sciences; 0604 Genetics; 0699 Other Biological Sciences; Chemical Physics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Computer Science & Mathematics
Publisher:	MDPI
SWORD Depositor:	A Symplectic
Date Deposited:	16 Aug 2022 14:57
Last Modified:	16 Aug 2022 15:00
DOI or ID number:	10.3390/ijms23158235
URI:	https://researchonline.ljmu.ac.uk/id/eprint/17401

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