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

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

Background: 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...

Full description

Bibliographic Details
Main Authors: Xuan Xu, Jessica Kawakami, Nuwan Indika Millagaha Gedara, Jim E Riviere, Emma Meyer, Gerald J Wyckoff, Majid Jaberi-Douraki
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2021-11-01
Series:eLife
Subjects:
data-driven methodology
pulmonary symptomology
hypertension
artificial intelligence
FDA ADE database
COVID-19-related pharmacovigilance
Online Access:https://elifesciences.org/articles/70734

Internet

https://elifesciences.org/articles/70734

Similar Items