Syndromic Surveillance Data Exchange and Analysis

症状监测数据交换和分析

• 批准号: 6673408
• 负责人: Joseph Marcus Overhage
• 金额: $ 60.61万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-30 至 2006-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6673408
• 关键词: artificial intelligence; bioterrorism /chemical warfare; clinical research; computer system design /evaluation; disasters; disease outbreaks; early diagnosis; geographic site; human data; human population study; public health

DESCRIPTION (provided by applicant): Most disasters are readily identifiable but health care disasters may not be as readily recognized, particularly early in their evolution. Syndromic surveillance, one kind of health care disaster, is the term coined for systems that use data that precede diagnosis to recognize disease outbreaks with sufficient specificity to warrant a public health response. Algorithms designed for early detection of health care disasters that rely solely on temporal trends may not be specific enough and several investigators have begun to incorporate geospatial clustering into the pattern recognition algorithms in order to improve specificity. Knowing the specific geographic location of the patient's home address would allow easy identification of a patient in many cases, however, we propose to develop and characterize pattern recognition algorithms that operate on deidentified data in order to protect patients' privacy. We will carry out this work using actual data from two different cities in order to increase the chance that the approach developed can be generalized. The overall approach would be to utilize established pattern recognition algorithms developed at Children's Hospital in Boston, and modify them as needed to accommodate use of deidentified data. Then we will compare the detection characteristics of the algorithm using identifiable data and deidentified data in order to determine the effect of using deidentified data.

描述（由申请人提供）： 大多数灾难都很容易识别，但医疗保健灾难可能不那么容易识别，特别是在其演变的早期。综合征监测是一种医疗灾难，是一个术语，用于使用诊断前的数据来识别疾病爆发的系统，具有足够的特异性，以保证公共卫生响应。专为早期检测医疗保健灾难，仅仅依靠时间趋势的算法可能不够具体，一些研究人员已经开始将地理空间聚类到模式识别算法，以提高特异性。了解患者家庭地址的具体地理位置可以在许多情况下轻松识别患者，但是，我们建议开发和表征模式识别算法，该算法对去识别数据进行操作，以保护患者的隐私。我们将使用来自两个不同城市的实际数据进行这项工作，以增加开发的方法可以推广的机会。总体方法是利用波士顿儿童医院开发的已建立的模式识别算法，并根据需要对其进行修改，以适应去识别数据的使用。然后，我们将使用可识别数据和去识别数据的算法的检测特性进行比较，以确定使用去识别数据的效果。