ArticlePloS one2024
AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 49 citations in OpenAlex.
- Precision pharmacology: deep learning infused ontological framework with E-GRU enhancement for tailored medicine prescriptions.Scientific reports · 2026Article
- Structured taxonomy and framework for developing medical benchmark in large language models derived from scoping review.NPJ digital medicine · 2026Article
- Classification of health product defect reports by deep learning.Scientific reports · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Identification and validation of respiratory virus immunization using natural language processing.Frontiers in digital health · 2026Article
- Exploring multimodal large language models on transthoracic Echocardiogram (TTE) tasks for cardiovascular decision support.Journal of biomedical informatics · 2025Article
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Identifying Adverse Drug Events in Clinical Text Using Fine-Tuned Clinical Language Models: Machine Learning Study.JMIR formative research · 2025Article
- Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.BMJ health & care informatics · 2025Article
- The Development Landscape of Large Language Models for Biomedical Applications.Annual review of biomedical data science · 2025Review
- Unveiling differential adverse event profiles in vaccines via LLM text embeddings and ontology semantic analysis.Journal of biomedical semantics · 2025Article
- Enhancing Relation Extraction for COVID-19 Vaccine Shot-Adverse Event Associations with Large Language Models.Research square · 2025Article
- Exploring Associations Between COVID-19 Bivalent Vaccines and Their Related Adverse Events: A Correlational Study.Research square · 2025Article
- Exploring Temporal and Spatial Characteristics of Serious Adverse Event Reports Following COVID-19 Bivalent Vaccines.Research square · 2025Article
- Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.Journal of biomedical informatics · 2025Article
- Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments.Drug safety · 2025Article
- Article
- Urban walkability through different lenses: A comparative study of GPT-4o and human perceptions.PloS one · 2025Article
- Developing electronic health records as a source of real-world data for veterinary pharmacoepidemiology.Frontiers in veterinary science · 2025Article
- Real-world pharmacovigilance reports of hepatitis A inactivated and hepatitis B (recombinant) vaccine: insights from disproportionality analysis of the vaccine adverse event reporting system.Frontiers in cellular and infection microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs' capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs' potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.