Standardizing adverse drug event reporting data.

Area of Science:

• Pharmacovigilance
• Biomedical Informatics
• Data Science

Background:

• The FDA Adverse Event Reporting System (AERS) contains valuable voluntary reports of adverse drug events (ADEs).
• Normalizing AERS data enhances its utility for drug safety signal detection and semantic interoperability with other data sources.

Purpose of the Study:

• To normalize the AERS database and create a publicly accessible, aggregated data source for adverse drug events.
• To improve the capacity for mining drug safety signals from AERS data.

Main Methods:

• Utilized RxNorm for drug normalization via the MedEx natural language processing tool.
• Employed a greedy algorithm to map drug information to National Drug File-Reference Terminology (NDF-RT) classes.
• Aggregated adverse events by mapping to Medical Dictionary for Regulatory Activities (MedDRA) Preferred Term (PT) and System Organ Class (SOC) codes.

Main Results:

• Developed the aggregated knowledge-enhanced AERS data mining set (AERS-DM) with 37,029,228 Drug-ADE records.
• Classified 71% of normalized drug concepts into 9 NDF-RT classes.
• Generated millions of unique pairs between RxNorm concepts and MedDRA PT/SOC codes.

Conclusions:

• An open-source Drug-ADE knowledge resource was created using normalized and aggregated data with standard biomedical ontologies.
• This resource is poised to aid the data mining research community in analyzing ADEs from AERS.