The Application of Big Data in Pharmacovigilance: A Systematic Review

DOI

10.2991/assehr.k.220110.102How to use a DOI?

Keywords

Big data application; Pharmacovigilance (PV); Adverse Drug Events (ADEs); Post-market drugs; Algorithmic Models

Abstract

Since the outbreak of coronavirus, public health has become a prevalent topic revolving around people’s daily life ever since. When the official announcement that the first mRNA COVID-19 vaccine candidates had successfully completed the clinical trials and was ready to be launched, the public remained deeply skeptical about the unknown contraindications that might cause severe immune compromise after administering the vaccine. Even though the mRNA vaccine was FDA-authorized, the person getting vaccinated could still be affected by adverse events after receiving the first dose. Mild symptoms could manifest quickly, and could be fatal under extreme circumstances after getting the first dose. Adverse drug events (ADEs) are commonly monitored throughout the clinical trials, to minimize any unintended consequences of specific medication use. Epidemiologists have reported a global rise in human diseases over the past few decades. The augmented demand for innovative drugs has naturally boosted drug manufacturing to become one of the most lucrative fields of the healthcare industry. Drug safety surveillance needs to be highlighted given that even a slight overdose could do harm to one’s body. As such, post-market drug safety must be monitored strictly and routinely. This paper provides an overview of introducing the existing big data applications in the global pharmacovigilance market, employing algorithmic models to predict ADEs of marketed drugs to effectively improve the therapeutic effect, and challenges presented in applying big data to post-market drug safety monitoring. The specific advantages of employing algorithmic models are similar to the six Vs of big data (volume, velocity, variety, veracity, validity and value) and are pivotal to the policy-making process. This study examines the popular commercially available pharmacovigilance tools that can be found online by investigating their models and services. Then, we delved into several algorithmic models to see the detailed procedure of how big data process complex information.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Mingshuai Han
PY  - 2022
DA  - 2022/01/28
TI  - The Application of Big Data in Pharmacovigilance: A Systematic Review
BT  - Proceedings of the 2021 International Conference on Public Art and Human Development ( ICPAHD 2021)
PB  - Atlantis Press
SP  - 533
EP  - 537
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220110.102
DO  - 10.2991/assehr.k.220110.102
ID  - Han2022
ER  -