15 citations · 19 across the 7 of their papers we have counts for
7 papers
Development of a European Union Time-Indexed Reference Dataset for Assessing the Performance of Signal Detection Methods in Pharmacovigilance using a Large Language Model
Maria Kefala, Jeffery L. Painter, Syed Tauhid Bukhari +1
Background: The identification of optimal signal detection methods is hindered by the lack of reliable reference datasets. Existing datasets do not capture when adverse events (AEs…
CTG-DB: An Ontology-Based Transformation of ClinicalTrials.gov to Enable Cross-Trial Drug Safety Analyses
Jeffery L. Painter, François Haguinet, Andrew Bate
ClinicalTrials .gov (CT .gov) is the largest publicly accessible registry of clinical studies, yet its registry-oriented architecture and heterogeneous adverse event (AE) terminolo…
Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events
François Haguinet, Jeffery L Painter, Gregory E Powell +2
We present a Bayesian dynamic borrowing (BDB) approach to enhance the quantitative identification of adverse events (AEs) in spontaneous reporting systems (SRSs). The method embeds…
Ontology-based Semantic Similarity Measures for Clustering Medical Concepts in Drug Safety
Jeffery L Painter, François Haguinet, Gregory E Powell +1
Semantic similarity measures (SSMs) are widely used in biomedical research but remain underutilized in pharmacovigilance. This study evaluates six ontology-based SSMs for clusterin…
PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction
Jeffery L Painter, Gregory E Powell, Andrew Bate
Reliable drug safety reference databases are essential for pharmacovigilance, yet existing resources like SIDER are outdated and static. We introduce PVLens, an automated system th…
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem
Joe B Hakim, Jeffery L Painter, Darmendra Ramcharran +6
Large language models (LLMs) are useful tools with the capacity for performing specific types of knowledge work at an effective scale. However, LLM deployments in high-risk and saf…
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL
Jeffery L. Painter, Venkateswara Rao Chalamalasetti, Raymond Kassekert +1
Objective: To enhance the efficiency and accuracy of information retrieval from pharmacovigilance (PV) databases by employing Large Language Models (LLMs) to convert natural langua…