Collaborative Research: SEI: Graph-based Mining of Public Health Data

公共卫生数据的自动化分析是一种迫切需要，但有效的分析必须超越单个数据点。收集的大部分数据都是结构性的，不仅包括实体，还包括实体之间的关系（例如空间、时间）。因此，需要开发专门针对这类结构数据的发现知识和学习概念的方法。一种基于图的数据挖掘技术，可以对表示为图的数据执行模式发现、概念学习和分层聚类。在Subdue系统中实施的这种方法在许多科学和工业数据库中取得了成功。拟议的工作将调查基于图的数据挖掘方法的可行性，作为公共卫生数据库和相关应用程序中发现的表示和微结构数据的基础。这项工作将有助于1)对公共卫生数据进行分析，探索数据点，数据点之间的关系，并整合来自相关领域的数据以加强结果，2)设计一个基于图的挖掘系统，可以以在线方式处理流数据，3)开发一种新的概念学习方法，处理嵌入在单个相互关联的图中的训练示例，4)构建一个工具集，可以提供流行病和其他公共卫生危机的早期检测和评估。该项目依赖于计算机科学家和公共卫生专家之间强有力的伙伴关系。德克萨斯大学阿灵顿分校和北德克萨斯大学健康科学中心之间的合作已经得到了两所学校的初步支持，合作将通过每月的研讨会和研究会议来促进。因此，该项目的成果将对计算机科学界产生影响，并对领域社区产生同等（如果不是更大的话）的影响。代码和数据将在互联网上广泛传播，结果将集成到课堂和基于图的数据挖掘的书中。

Automated analysis of public health data represents a critical need, but effective analysis must look beyond individual data points. Much of the data that is collected is structural, consisting not only of entities but also of relationships (e.g., spatial,temporal) between the entities. As a result, a need exists to develop methods for discovering knowledge and learning concepts specifically for this type of structural data. A graph-based data mining technique that can perform pattern discovery, concept learning, and hierarchical clustering on data represented as graphs. This approach, implemented in the Subdue system, has demonstrated success in a numbeof scientific and industrial databases. The proposed effort will investigate the viability ofgraph-based data mining approach as a foundation for representing and ministructural data found in public health databases and related applications.The effort will contribute 1) an analysis of public health data that explores datapoints, relationships between the data points, and integration of data from relateddomains to strengthen the results, 2) design of a graph-based mining system that can handle streaming data in an online fashion, 3) development of a new approach to concept learning that processes training examples embedded n a single interconnected graph, and 4) construction of a toolset that can provide early detection and assessment of epidemics and other public health crises.The project depends on a strong partnership between computer scientists and an expert in public health. A collaboration between the University of Texas at Arlington and the University of North Texas Health Science Center has already received initial support from the two schools The collaboration will be fostered through monthly seminars and research meetings. The results of this project will thus have an impact on the computerscience community and an equal, if not greater, impact on the domain community The code and data will be available for general dissemination over the Internet, and results will be integrated into the classroom and into a book on graph-based data mining.