从维基百科条目中获取疾病数据
Eliciting Disease Data from Wikipedia Articles
摘要：
传统的疾病监测系统有几个缺点，包括报告滞后和技术陈旧，这导致了基于互联网的疾病监测系统的发展。互联网系统对疾病暴发特别有吸引力，因为它们可以近实时地提供数据，并可由全球各地的个人进行验证。然而，大多数现有系统都侧重于疾病监测，并没有为决策者或研究人员提供数据储存库。为了填补这一空白，我们分析了维基百科的文章内容。我们演示了如何训练命名实体识别器来标记文章叙述中的病例数、死亡数和住院数，这些病例数和住院数的F1得分为0.753。我们还显示，使用2014年西非埃博拉病毒疾病流行的文章作为案例研究，有详细的时间序列数据，不断更新，密切配合地面实况数据。我们认为，维基百科可以用来创建第一个社区驱动的开源新兴疾病检测，监测和存储库系统。
英文摘要：
Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Internet systems are particularly attractive for disease outbreaks because they can provide data in near real-time and can be verified by individuals around the globe. However, most existing systems have focused on disease monitoring and do not provide a data repository for policy makers or researchers. In order to fill this gap, we analyzed Wikipedia article content. We demonstrate how a named-entity recognizer can be trained to tag case counts, death counts, and hospitalization counts in the article narrative that achieves an F1 score of 0.753. We also show, using the 2014 West African Ebola virus disease epidemic article as a case study, that there are detailed time series data that are consistently updated that closely align with ground truth data. We argue that Wikipedia can be used to create the first community-driven open-source emerging disease detection, monitoring, and repository system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
