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An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News
Sergio Consoli1, Pietro Coletti2,3, Peter V Markov2,4
1European Commission, Joint Research Centre (JRC), Ispra, Italy. sergio.consoli@ec.europa.eu.
Scientific Data
|June 10, 2025
Summary
This study uses generative AI and multiple Large Language Models (LLMs) to extract epidemiological data from WHO Disease Outbreak News, creating a knowledge graph for enhanced disease surveillance and public health research.
Area of Science:
- Public Health
- Epidemiology
- Artificial Intelligence
Background:
- The convergence of artificial intelligence (AI) and readily available news/social media data presents new opportunities for epidemiological surveillance.
- The World Health Organization (WHO) Disease Outbreak News (DONs) provides crucial information on global disease outbreaks.
Purpose of the Study:
- To leverage generative AI and Large Language Models (LLMs) for extracting epidemiological insights from WHO DONs.
- To develop a structured knowledge graph (eKG) for nuanced representation of public health domain knowledge.
Main Methods:
- An ensemble approach utilizing multiple LLMs to process and extract epidemiological information from WHO DONs.
- Construction of an epidemiological knowledge graph (eKG) to organize extracted data.
Main Results:
- A novel dataset derived from WHO DONs, structured within the eKG.
- Development of services and tools for accessing and utilizing the eKG data.
Conclusions:
- The developed eKG and associated tools offer innovative resources for epidemiological research and disease outbreak analysis.
- This approach enhances the potential for real-time surveillance and response to public health emergencies.
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