Syndromic Surveillance Data Exchange and Analysis

症状监测数据交换和分析

• 批准号: 6800057
• 负责人: SHAUN J GRANNIS
• 金额: $ 60.72万
• 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/6800057
• 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)： 大多数灾难是容易识别的，但卫生保健灾难可能不那么容易识别，特别是在其演变的早期。综合症监测是医疗灾难的一种，这个术语是指在诊断之前使用数据来识别疾病爆发的系统，这些系统具有足够的特异性，足以保证公共卫生应对。仅根据时间趋势设计的用于早期检测医疗保健灾难的算法可能不够具体，一些研究人员已开始将地理空间聚类纳入模式识别算法，以提高特异性。知道患者家庭住址的特定地理位置将有助于在许多情况下轻松识别患者，然而，我们建议开发和表征模式识别算法，这些算法对识别出的数据进行操作，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，以增加所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，并根据需要进行修改，以适应识别数据的使用。然后，我们将比较算法使用可识别数据和识别数据的检测特性，以确定使用识别数据的效果。