Work

Data Mining Pipeline for COVID-19 Vaccine Safety Analysis Using a Large Electronic Health Record.

Year: 2023

Type: article

Abstract: de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine. We then ranked the diagnosis codes using the adverse event rates and adjusted odds ratio based on the self-controlled case series analysis. Using inverse probability of censoring weighting, we estimated the right-censored time-to-event records. Our results show that the COVID-19 vaccine has a similar adverse events rate to the influenza vaccine. We found 20 types of potential COVID-19 vaccine-related adverse events that may need further investigation.

Source: PubMed Central

Authors: Yan Huang, Xiaojin Li, Deepa Dongarwar, Hulin Wu, Guo-Qiang Zhang

Institution: The University of Texas Health Science Center at Houston

Language: English

Topic: Pharmacovigilance and Adverse Drug Reactions

Subfield: Toxicology

Field: Pharmacology, Toxicology and Pharmaceutics

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Neurological Disorders and Stroke

Award: SCH: Neurophysiological AI-Ready Data Resource