There is great interest in the application of 'artificial intelligence'(AI) to pharmacovigilance
(PV). Although US FDA is broadly exploring the use of AI for PV, we focus on the application …

Monitoring drug safety is a central concern throughout the drug life cycle. Information about
toxicity and adverse events is generated at every stage of this life cycle, and stakeholders …

Introduction The rapidly expanding size of the Food and Drug Administration's (FDA)
Adverse Event Reporting System database requires modernized pharmacovigilance …

The US Food and Drug Administration Adverse Event Reporting System (FAERS) contains
over 24 million individual case safety reports (ICSRs). In this research project, we evaluated …

There has been growing interest of using machine learning (ML) methods with real-world
data (RWD) to generate real-world evidence (RWE) to support regulatory decisions. In the …

Introduction Missing age presents a significant challenge when evaluating individual case
safety reports (ICSRs) in the FDA Adverse Event Reporting System (FAERS). When age is …

Incident report triage is a common endeavor in many industry sectors, often coupled with
serious public safety implications. For example, at the US Food and Drug Administration …

Instance-based Incident Analysis (IIA)–a labor intensive and error-prone task–requires
analysts to review text-based reports of incidents, where each may be evidence of a larger …

Drug safety analysts at the US Food & Drug Administration analyze medication error reports
submitted to the Adverse Event Reporting System (FAERS) to detect and prevent detrimental …

To detect harmful medication errors and inform regulatory actions, the US Food & Drug
Administration uses the FAERS spontaneous reporting system to collect medication error …