用AI从世卫组织疫情通报中提取知识，构建动态更新的流行病学知识图谱。
An Epidemiological Knowledge Graph extracted from the World Health Organization's Disease Outbreak News
- 融合多模型大语言模型，自动解析世卫组织疫情通报内容。
- 构建每日更新的知识图谱eKG，涵盖全球疫情事件与响应机制。
- 开放数据服务与工具，支持疫情监测与科研分析，适合公共卫生研究者使用。
人工智能的快速发展，结合社交媒体和新闻数据的日益丰富，正在推动流行病学与公共卫生研究进入关键转折点。我们利用生成式AI，通过集成多个大语言模型（LLMs）的方法，从世界卫生组织（WHO）的疾病爆发新闻（Disease Outbreak News, DONs）中提取具有行动价值的流行病学信息。DONs是世卫组织定期编纂的全球疫情报告及应对决策过程记录。提取的信息被整合为每日更新的数据集与知识图谱，称为eKG，旨在提供对公共卫生领域知识的精细化表达。本文介绍该新数据集的概况，描述eKG的结构，并说明用于访问和使用数据的服务与工具。这些创新数据资源为流行病学研究、疫情分析与监测开辟了全新可能性。
原文摘要 · Abstract (English)
The rapid evolution of artificial intelligence (AI), together with the increased availability of social media and news for epidemiological surveillance, are marking a pivotal moment in epidemiology and public health research. Leveraging the power of generative AI, we use an ensemble approach which incorporates multiple Large Language Models (LLMs) to extract valuable actionable epidemiological information from the World Health Organization (WHO) Disease Outbreak News (DONs). DONs is a collection of regular reports on global outbreaks curated by the WHO and the adopted decision-making processes to respond to them. The extracted information is made available in a daily-updated dataset and a knowledge graph, referred to as eKG, derived to provide a nuanced representation of the public health domain knowledge. We provide an overview of this new dataset and describe the structure of eKG, along with the services and tools used to access and utilize the data that we are building on top. These innovative data resources open altogether new opportunities for epidemiological research, and the analysis and surveillance of disease outbreaks.
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