Data mining methodology for response to hypertension symptomology-application to COVID-19-related pharmacovigilance.

ABSTRACT: Potential therapy and confounding factors including typical co-administered medications, patient's disease states, disease prevalence, patient demographics, medical histories, and reasons for prescribing a drug often are incomplete, conflicting, missing, or uncharacterized in spontaneous adverse drug event (ADE) reporting systems. These missing or incomplete features can affect and limit the application of quantitative methods in pharmacovigilance for meta-analyses of data during randomized clinical trials. Data from patients with hypertension were retrieved and integrated from the FDA Adverse Event Reporting System; 134 antihypertensive drugs out of 1131 drugs were filtered and then evaluated using the empirical Bayes geometric mean (EBGM) of the posterior distribution to build ADE-drug p

SUBMITTER: Xu X 

PROVIDER: S-EPMC8754433 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature