ArticleDrug safety2024
Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing.
Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-assisted mRNA vaccine pharmacovigilance: a systematic review of multi-source real-world data.BMC medical informatics and decision making · 2026Pooled it
- Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical Safety Alerts.Drug safety · 2026Article
- Biomedical Large Language Models and Prompt Engineering for Causality Assessment of Individual Case Safety Reports in Pharmacovigilance.Pharmaceutical research · 2026Article
- How is AI developing in pharmacovigilance?Therapeutic advances in drug safety · 2026Article
- Off-the-Shelf Large Language Models for Causality Assessment of Individual Case Safety Reports: A Proof-of-Concept with COVID-19 Vaccines.Drug safety · 2025Article
- Artificial Intelligence: Applications in Pharmacovigilance Signal Management.Pharmaceutical medicine · 2025Review
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- Orchestrating generative AI in pharmacovigilance: predicting and preempting the unpredictable.Therapeutic advances in drug safety · 2025Review
- Can large language models detect drug-drug interactions leading to adverse drug reactions?Therapeutic advances in drug safety · 2025Article
- The role of MedDRA in global pharmacovigilance and public health during COVID-19: pandemic preparedness and response.Frontiers in drug safety and regulation · 2025Article
- A national pharmacovigilance centre perspective on pandemic preparedness -Frontiers in drug safety and regulation · 2025Article
- The WHO Algorithm for Causality Assessment of Adverse Effects Following Immunization with Genetic-Based Anti-COVID-19 Vaccines: Pitfalls and Suggestions for Improvement.Journal of clinical medicine · 2024Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
- Editorial: Traditional and innovative approaches for signal detection.Frontiers in drug safety and regulation · 2024Article
- PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as 'statistical alerts') generated is expected.
objectivesThe objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept.
methodsThe VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+.
resultsOverall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs.
conclusionOur approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management.
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