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

Author

Blind, Jason ; Das, Subrata

Author_Institution

Charles River Analytics, Inc., Cambridge

fYear

2007

fDate

9-12 July 2007

Firstpage

1

Lastpage

7

Abstract

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.

Keywords

belief networks; diseases; medical computing; sensor fusion; surveillance; unsupervised learning; biosurveillance; data fusion approach; disease outbreak detection; dynamic Bayesian networks; latent semantic analysis; public health system databases; two-level fusion architecture; unsupervised clustering; Bayesian methods; Bioterrorism; Databases; Diseases; Feeds; Influenza; Medical diagnostic imaging; Public healthcare; Rivers; System testing; Biosurveillance; Clustering; Data Fusion; Dynamic Bayesian Networks; Latent Semantic Analysis; Unsupervised Learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2007 10th International Conference on

Conference_Location

Quebec, Que.

Print_ISBN

978-0-662-45804-3

Electronic_ISBN

978-0-662-45804-3

Type

conf

DOI

10.1109/ICIF.2007.4408073

Filename

4408073