A study into detection of bio-events in multiple streams of surveillance data

被引：0
|
作者：
Roure, Josep [1 ]
Dubrawski, Artur [1 ]
Schneider, Jeff [1 ]
机构：
[1] Carnegie Mellon Univ, Auton Lab, Pittsburgh, PA 15213 USA
来源：
INTELLIGENCE AND SECURITY INFORMATICS: BIOSURVEILLANCE, PROCEEDINGS | 2007年 / 4506卷
基金：
美国农业部;
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper reviews the results of a study into combining evidence from multiple streams of surveillance data in order to improve timeliness and specificity of detection of bio-events. In the experiments we used three streams of real food- and agriculture-safety related data that is being routinely collected at slaughter houses across the nation, and which carry mutually complementary information about potential outbreaks of bio-events. The results indicate that: (1) Non-specific aggregation of p-values produced by event detectors set on individual streams of data can lead to superior detection power over that of the individual detectors, and (2) Design of multi-stream detectors tailored to the particular characteristics of the events of interest can further improve timeliness and specificity of detection. In a practical setup, we recommend combining a set of specific multi-stream detectors focused on individual types of predictable and definable scenarios of interest, with non-specific multi-stream detectors, to account for both anticipated and emerging types of bio-events.
引用
收藏
页码：124 / +
页数：3
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