Improving Syndromic Surveillance by Data Integration

通过数据集成改进症状监测

• 批准号: 7351841
• 负责人: AL OZONOFF
• 金额: $ 35.17万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2009-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7351841
• 关键词: Improving; Syndromic; Surveillance; Data; Integration

The term "syndromic surveillance" is a generic term applied to a variety of newly developed methods in public health practice. For our purposes, syndromic surveillance refers to the automated collection and analysis in near-real-time of electronic health outcome data. Used in this way, syndromic surveillance sits within a broader category of "biosurveillance", the routine collection and analysis of electronic data falling outside of the classical surveillance paradigm. We propose a research program to improve the performance of aberration detection methods for syndromic surveillance using statistical methods of data integration. Our program focuses on three main areas of potential improvement: temporal modeling, spatio-temporal clustering, and integration of multiple data streams. We also include a research translation component, in order to ensure that the results of research will be of practical use to health departments and other practitioners of syndromic surveillance. Our three specific aims are: 1) To develop and improve temporal modeling for syndromic surveillance, using improved seasonal models and Hidden Markov Models (HMMs); 2) To investigate and evaluate data integration methods, including spatio-temporal clustering and multiple data source integration; 3) To develop PHIN-compliant software for use by local health departments and syndromic surveillance practitioners.

术语“综合征监视”是一种通用术语，适用于各种新开发的方法 公共卫生实践。出于我们的目的，综合症监测是指自动收集， 分析电子健康结果数据的近实时。以这种方式使用综合症监测 在更广泛的“生物监视”类别中，对电子数据下降的常规收集和分析 在古典监视范式之外。 我们提出了一项研究计划，以提高综合症的畸变检测方法的性能 使用数据集成的统计方法进行监视。我们的计划着重于三个主要领域 潜在的改进：时间建模，时空聚类和多个数据集成 流。我们还包括一个研究翻译部分，以确保研究结果 将对卫生部门和其他综合症监测的从业人员进行实际使用。 我们的三个具体目的是：1）使用综合症监测的时间建模和改进 改进的季节性模型和隐藏的马尔可夫模型（HMM）； 2）调查和评估数据 集成方法，包括时空聚类和多个数据源集成； 3）开发 符合PHIN的软件，可用于当地卫生部门和综合症监测从业人员使用。