An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News
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View abstract on PubMed
Summary
This summary is machine-generated.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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