# PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction

arXiv: https://arxiv.org/abs/2503.20639 · HTML: https://mlsanity.com/paper/2503.20639/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Built on by (most cited first, 2 of 2)

- [Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events](https://mlsanity.com/paper/2504.12052/) (2025)
- [CTG-DB: An Ontology-Based Transformation of ClinicalTrials.gov to Enable Cross-Trial Drug Safety Analyses](https://mlsanity.com/paper/2603.15936/) (2026)

## Abstract (arXiv)

Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduce PVLens, an automated system that extracts labeled safety information from FDA Structured Product Labels (SPLs) and maps terms to MedDRA. PVLens integrates automation with expert oversight through a web-based review tool. In validation against 97 drug labels, PVLens achieved an F1 score of 0.882, with high recall (0.983) and moderate precision (0.799). By offering a scalable, more accurate and continuously updated alternative to SIDER, PVLens enhances real-time pharamcovigilance with improved accuracy and contemporaneous insights.