Epidemic Information Extraction for Event-Based Surveillance using Large Language Models

This paper presents a novel approach to epidemic surveillance, leveraging the power of Artificial Intelligence and Large Language Models (LLMs) for effective interpretation of unstructured big data sources, like the popular ProMED and WHO Disease Outbreak News. We explore several LLMs, evaluating th...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Consoli, Sergio, Markov, Peter, Stilianakis, Nikolaos I, Bertolini, Lorenzo, Antonio Puertas Gallardo, Ceresa, Mario
Format: Article
Language:English
Subjects:
Artificial intelligence
Epidemics
Information retrieval
Large language models
Surveillance
Unstructured data
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a novel approach to epidemic surveillance, leveraging the power of Artificial Intelligence and Large Language Models (LLMs) for effective interpretation of unstructured big data sources, like the popular ProMED and WHO Disease Outbreak News. We explore several LLMs, evaluating their capabilities in extracting valuable epidemic information. We further enhance the capabilities of the LLMs using in-context learning, and test the performance of an ensemble model incorporating multiple open-source LLMs. The findings indicate that LLMs can significantly enhance the accuracy and timeliness of epidemic modelling and forecasting, offering a promising tool for managing future pandemic events.
ISSN:2331-8422
DOI:10.48550/arxiv.2408.14277