HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports.
HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports.
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DOI：
10.1197/jamia.m2544
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发表时间：
2008-03
影响因子：
6.4
通讯作者：
Bronwnstein, John S.
中科院分区：
文献类型：
--
作者：
Freifeld, Clark C.;Mandl, Kenneth D.;Ras, Ben Y.;Bronwnstein, John S.
非结构化的电子信息来源，如新闻报道，被证明是公共卫生监督的宝贵投入。然而，要跟上当前疾病爆发的步伐，就需要搜索不断增加的不同新闻来源和警报服务，导致信息过载。我们的目标是通过HealthMap.org网络应用程序来应对这一挑战，这是一个用于查询、过滤、整合和可视化疾病爆发非结构化报告的自动化系统。本报告介绍了HealthMap的设计原则、软件架构和实施情况，并讨论了主要挑战和未来计划。我们描述了HealthMap从各种来源收集和整合疫情数据的过程，包括新闻媒体（例如，谷歌新闻），专家策划的帐户（例如，ProMED Mail），并验证了官方警报。通过使用文本处理算法，该系统按位置和疾病对警报进行分类，然后将其叠加在交互式地理地图上。我们根据纠正错误分类所需的人工管理水平来衡量分类算法的准确性，并检查地理覆盖范围。作为该系统评估的一部分，我们分析了778份报告与健康地图，代表87种疾病类别和89个国家。自动分类器的准确率为84%，在管理系统处理的大量信息方面表现出了显著的实用性。ProMED警报的准确率为91%，而Google新闻报道的准确率为81%，因为ProMED消息遵循更规则的结构。健康地图是一个有用的免费和开放的资源，采用文本处理算法，通过用户友好的界面确定重要的疾病爆发信息。
Unstructured electronic information sources, such as news reports, are proving to be valuable inputs for public health surveillance. However, staying abreast of current disease outbreaks requires scouring a continually growing number of disparate news sources and alert services, resulting in information overload. Our objective is to address this challenge through the HealthMap.org Web application, an automated system for querying, filtering, integrating and visualizing unstructured reports on disease outbreaks. This report describes the design principles, software architecture and implementation of HealthMap and discusses key challenges and future plans. We describe the process by which HealthMap collects and integrates outbreak data from a variety of sources, including news media (e.g., Google News), expert-curated accounts (e.g., ProMED Mail), and validated official alerts. Through the use of text processing algorithms, the system classifies alerts by location and disease and then overlays them on an interactive geographic map. We measure the accuracy of the classification algorithms based on the level of human curation necessary to correct misclassifications, and examine geographic coverage. As part of the evaluation of the system, we analyzed 778 reports with HealthMap, representing 87 disease categories and 89 countries. The automated classifier performed with 84% accuracy, demonstrating significant usefulness in managing the large volume of information processed by the system. Accuracy for ProMED alerts is 91% compared to Google News reports at 81%, as ProMED messages follow a more regular structure. HealthMap is a useful free and open resource employing text-processing algorithms to identify important disease outbreak information through a user-friendly interface.