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

• Published in: International journal of molecular sciences Vol. 23; no. 15; p. 8235
• Main Authors: Flora, James, Khan, Wasiq, Jin, Jennifer, Jin, Daniel, Hussain, Abir, Dajani, Khalil, Khan, Bilal
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.07.2022; MDPI
• Subjects: Adverse Drug Reaction Reporting Systems; Adverse events; Age groups; association rule mining; Case studies; Chi-square test; Child; Chills; Clustering; Coronaviruses; COVID-19; COVID-19 - epidemiology; COVID-19 - prevention & control; COVID-19 vaccines; COVID-19 Vaccines - adverse effects; Datasets; Dyspnea; Erythema; Fainting; Fecal incontinence; Female; Fever; Gender; Guillain-Barre syndrome; Headache; Headaches; Humans; Immunization; Influenza; Learning algorithms; Machine Learning; Male; Manufacturers; Muscles; Nausea; Pain; Pain - chemically induced; Penicillin; Penicillins; Pruritus; Self organizing maps; Statistical analysis; Surveys; United States; Urticaria; vaccine development; Vaccines; Vaccines - adverse effects; VAERS; Vomiting; United States; United States > US; COVID-19; VAERS; bipartite graphs; vaccine analysis workflow; vaccine development; hierarchical clustering; adverse events; self-organizing maps; association rule mining
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms23158235

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).