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Using Probabilistic Record Linkage of Structured and Unstructured Data to Identify Duplicate Cases in Spontaneous
Kory Kreimeyer1, David Menschik2, Scott Winiecki2
1Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD, 20993-0002, USA. Kory.Kreimeyer@fda.hhs.gov.
Drug Safety
|March 16, 2017
Summary
A new algorithm effectively identifies duplicate adverse event reports in VAERS and FAERS, improving safety analysis. It achieved high precision in detecting duplicates, reducing spurious signals in pharmacovigilance data.
Area of Science:
- Pharmacovigilance and Drug Safety
- Health Informatics
- Natural Language Processing in Healthcare
Background:
- Duplicate case reports in spontaneous adverse event reporting systems challenge safety analysis.
- Duplicate data can create misleading signals in pharmacovigilance, impacting data mining accuracy.
- Efficient identification of duplicate reports is crucial for reliable safety surveillance.
Purpose of the Study:
- To develop and evaluate a probabilistic record linkage algorithm for identifying duplicate adverse event reports.
- To assess the algorithm's performance in the US Vaccine Adverse Event Reporting System (VAERS) and the US Food and Drug Administration Adverse Event Reporting System (FAERS).
- To determine the contribution of narrative text and a novel duplicate confidence value to algorithm accuracy.
Main Methods:
- Developed a probabilistic record linkage algorithm using structured data and narrative text from adverse event reports.
- Incorporated clinical and temporal information extracted via natural language processing (NLP) using the Event-based Text-mining of Health Electronic Records system.
- Calculated a novel duplicate confidence value using a rule-based empirical approach comparing report criteria.
Main Results:
- The algorithm identified 77% of known duplicate pairs in VAERS with 95% precision and 13% in FAERS with 100% precision.
- Narrative text analysis did not significantly improve automated classification accuracy for either system.
- The empirical duplicate confidence value enhanced performance by reducing false-positive identifications in both VAERS and FAERS.
Conclusions:
- The algorithm effectively identifies duplicate reports in VAERS, supporting semi-automated review processes.
- While narrative text was not key for automated detection, it is vital for manual review support in a semi-automated system.
- The developed algorithm and confidence value show promise for improving the efficiency and accuracy of pharmacovigilance data analysis.
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