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- W4205151673 abstract "Adverse events (AEs) following COVID vaccination have been intensely monitored. In our study, we developed a natural language processing system to analyze data from a spontaneous reporting system - Vaccine Adverse Event Reporting System and detect signals of AEs following administration of COVID vaccines. Our system included several components to magnify novel and rare AEs, including 1) excluding COVID positive patients, 2) excluding sentences discussing disease history or family history, 3) standardizing symptom concepts into 30 major AEs, 4) using influenza vaccine recipients as control group when calculating reporting odds ratio. We identified several cardiovascular and inflammatory-related AEs that demonstrated high odds ratio. We demonstrated our system can serve as a complementary system to identify and monitor AEs outside of pre-defined outcomes routinely monitored by existing databases or projects." @default.
- W4205151673 created "2022-01-25" @default.
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- W4205151673 date "2021-12-09" @default.
- W4205151673 modified "2023-09-28" @default.
- W4205151673 title "COVID Vaccine and Cardiovascular Risks: A Natural Language Analysis of Vaccine Adverse Event Reports" @default.
- W4205151673 doi "https://doi.org/10.1109/bibm52615.2021.9669540" @default.
- W4205151673 hasPublicationYear "2021" @default.
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