Disease outbreak detection and tracking for biosurveillance: A data fusion approach

被引：0

作者：

Blind, Jason ^{[1
]}

Das, Subrata ^{[1
]}

机构：

[1] Charles River Analyt Inc, Cambridge, MA 02138 USA

来源：

2007 PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION, VOLS 1-4 | 2007年

关键词：

biosurveillance; clustering; data fusion; dynamic Bayesian networks; latent semantic analysis; unsupervised learning;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we present an application that utilizes a novel two-level fusion architecture to detect and track disease outbreaks across public health system databases. In the first fusion level, collected data is used to detect and track indicative bio-events using latent semantic analysis and unsupervised clustering. In the second fusion level, clusters produced via the first are used to feed dynamic Bayesian networks which assess outbreak type and state. We train and test our system using data from a 200K+ free-text emergency department (ED) chief complaint record set.

引用

页码：798 / 804

页数：7

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