用AI实时分析网络新闻，快速发现潜在疫情，助力疾控中心及时干预。
Health Sentinel: An AI Pipeline For Real-time Disease Outbreak Detection
- 构建多阶段信息抽取流水线，融合机器学习与非机器学习方法。
- 处理超3亿篇新闻，识别超9.5万起健康事件，其中3500起被专家列为潜在疫情。
- 已部署于印度国家疾控中心，适合公共卫生监测与应急响应团队使用。
早期发现疾病暴发对确保卫生部门及时干预至关重要。由于传统指标监测面临挑战，监控在线媒体等非正式来源日益流行。然而，每日大量新闻文章使得人工筛选不切实际。为此，我们提出 Health Sentinel：一个结合机器学习与非机器学习方法的多阶段信息提取流水线，从在线文章中提取关于疾病暴发或其他异常健康事件的结构化信息。这些事件数据已提供给位于德里的国家疾病控制中心（NCDC）的媒体扫描与验证小组（MSVC）进行分析、解读，并向地方机构传播以实现及时干预。自2022年4月起，Health Sentinel 已处理超过3亿篇新闻文章，识别出覆盖印度的逾9.5万条独特健康事件，其中超过3,500条经由NCDC公共卫生专家筛选为潜在暴发事件。
原文摘要 · Abstract (English)
Early detection of disease outbreaks is crucial to ensure timely intervention by the health authorities. Due to the challenges associated with traditional indicator-based surveillance, monitoring informal sources such as online media has become increasingly popular. However, owing to the number of online articles getting published everyday, manual screening of the articles is impractical. To address this, we propose Health Sentinel. It is a multi-stage information extraction pipeline that uses a combination of ML and non-ML methods to extract events-structured information concerning disease outbreaks or other unusual health events-from online articles. The extracted events are made available to the Media Scanning and Verification Cell (MSVC) at the National Centre for Disease Control (NCDC), Delhi for analysis, interpretation and further dissemination to local agencies for timely intervention. From April 2022 till date, Health Sentinel has processed over 300 million news articles and identified over 95,000 unique health events across India of which over 3,500 events were shortlisted by the public health experts at NCDC as potential outbreaks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。