Utilizing Deep Learning for Detecting Adverse Drug Events in Structured and Unstructured Regulatory Drug Data Sets.
Journal:
Pharmaceutical medicine
Published Date:
Jul 24, 2022
Abstract
BACKGROUND: The US Food and Drug Administration (FDA) collects and retains several data sets on post-market drugs and associated adverse events (AEs). The FDA Adverse Event Reporting System (FAERS) contains millions of AE reports submitted by the public when a medication is suspected to have caused an AE. The FDA monitors these reports to identify drug safety issues that were undetected during the premarket evaluation of these products. These reports contain patient narratives that provide information regarding the AE that needs to be coded using standardized terminology to enable aggregation of reports for further review. Additionally, the FDA collects structured drug product labels (SPLs) that facilitate standardized distribution of information regarding marketed medical products. Manufacturers are currently not required to code labels with associated AEs.