Improving Syndromic Surveillance by Data Integration

通过数据集成改进症状监测

• 批准号: 7098641
• 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 2008-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7098641
• 关键词: automated data processing; computer program /software; computer system design /evaluation; data collection methodology /evaluation; data management; health care facility information system; human data; mathematical model; method development; model design /development; public health; statistics /biometry

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.

“症状监测”一词是一个通用术语，适用于各种新开发的方法 公共卫生实践。就我们的目的而言，症状监测是指自动收集和 近乎实时地分析电子健康结果数据。以这种方式使用，症状监测就可以 在更广泛的“生物监测”范畴内，电子数据的常规收集和分析正在下降 在经典监视范式之外。 我们提出了一项研究计划来提高综合症像差检测方法的性能 使用数据整合统计方法进行监测。我们的计划重点关注三个主要领域 潜在的改进：时间建模、时空聚类和多个数据的集成 溪流。我们还包括研究翻译部分，以确保研究结果 将对卫生部门和其他症状监测从业人员具有实际用途。 我们的三个具体目标是：1）开发和改进症状监测的时间模型，使用 改进的季节性模型和隐马尔可夫模型（HMM）； 2）调查和评估数据 集成方法，包括时空聚类和多数据源集成； 3）开发 符合 PHIN 标准的软件，供当地卫生部门和症状监测从业人员使用。