Deduplicating the FDA adverse event reporting system with a novel application of network-based grouping

Abstract

Objective

To improve the reliability of data mining for product safety concerns in the Food and Drug Administration’s (FDA) Adverse Event Reporting System (FAERS) by robustly identifying duplicate reports describing the same patient experience.

Materials and methods

A duplicate detection algorithm based on a probabilistic record linkage algorithm, including features extracted from report narratives, and designed to support FAERS case safety review as part of the Information Visualization Platform (InfoViP) has been upgraded into a full deduplication pipeline for the entire FAERS database. The pipeline contains several new and updated components, including a network analysis-based community detection routine for breaking up sparsely connected groups of duplicates constructed from chains of pairwise comparisons. The pipeline was applied to all 29 million FAERS reports to assemble groups of duplicate cases.

Results

The pipeline was evaluated on 12 human expert adjudicated data sets with a total of 2300 reports and was found to have better overall performance than the current tool used at the FDA for labeling duplicates on 10 of them, with F1 scores ranging from 0.36 to 0.93, with half above 0.75. Because minimizing false discovery increases human expert review efficiency, the improved deduplication pipeline was applied to all historic and daily incoming FAERS reports at FDA and identified about 5 million reports as duplicates.

Conclusions

The InfoViP deduplication pipeline is operating at FDA to identify duplicate case reports in FAERS and provide deduplicated input for improved efficiency and accuracy of safety review operations like adverse event data mining calculations.