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Revisiting Disproportionality: Prescription-Adjusted and TF-IDF-Inspired Metrics for Post-Market ADR Detection
Ijay Kaz-Onyeakazi1, Heejun Kim1
1University of North Texas, Denton, Texas, United States.
AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 23, 2026
Summary
This study introduces novel metrics for detecting adverse drug reactions (ADRs) in post-market surveillance, improving pharmacovigilance by integrating prescription data. The EF-IDF method showed superior performance in identifying ADR signals for ADHD medications.
Area of Science:
- Pharmacovigilance and Drug Safety
- Computational Pharmacoepidemiology
- Health Informatics
Background:
- Post-market surveillance for adverse drug reactions (ADRs) faces challenges due to underreporting and lack of drug utilization data.
- Existing signal detection methods may not adequately account for variations in drug prescription volumes.
Purpose of the Study:
- To develop and evaluate novel signal detection metrics for ADRs that incorporate drug utilization data.
- To improve the accuracy and efficiency of pharmacovigilance by addressing data limitations.
Main Methods:
- Proposed three new signal detection metrics: EF-IDF (TF-IDF-inspired) and two prescription-adjusted measures.
- Integrated prescription data from Bloomberg Intelligence with the FDA Adverse Event Reporting System (FAERS) data.
- Evaluated metric performance using ADHD medications as a case study, focusing on precision-at-10% for 12 ingredients.
Main Results:
- The EF-IDF metric achieved the highest mean precision (0.56), outperforming traditional proportional reporting ratio (PRR) and other prescription-adjusted metrics.
- Prescription volume was found to negatively influence all evaluated metrics, highlighting the importance of contextual factors in ADR detection.
Conclusions:
- Bias-aware, data-integrated methods like EF-IDF offer significant improvements for ADR signal detection in pharmacovigilance.
- Future research should focus on temporal modeling and more specific prescription data for enhanced ADR identification.
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