Linked Life Data

15714642

Source:http://linkedlifedata.com/resource/pubmed/id/15714642

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2005-2-15
pubmed:abstractText
The outbreak-detection performance of a syndromic surveillance system can be measured in terms of its ability to detect signal (i.e., disease outbreak) against background noise (i.e., normally varying baseline disease in the region). Such benchmarking requires training and the use of validation data sets. Because only a limited number of persons have been infected with agents of biologic terrorism, data are generally unavailable, and simulation is necessary. An approach for evaluation of outbreak-detection algorithms was developed that uses semisynthetic data sets to provide real background (which effectively becomes the noise in the signal-to-noise problem) with artificially injected signal. The injected signal is defined by a controlled feature set of variable parameters, including size, shape, and duration.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1545-861X
pubmed:author
pubmed:issnType
Electronic
pubmed:day
24
pubmed:volume
53 Suppl
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
130-6
pubmed:dateRevised
2008-2-14
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Measuring outbreak-detection performance by using controlled feature set simulations.
pubmed:affiliation
Division of Emergency Medicine, Children's Hospital Boston, 300 Longwood Avenue, Boston, MA 02115, USA. Kenneth.Mandl@childrens.harvard.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't