| pubmed-article:16018815 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16018815 | lifeskim:mentions | umls-concept:C0525063 | lld:lifeskim |
| pubmed-article:16018815 | lifeskim:mentions | umls-concept:C0012652 | lld:lifeskim |
| pubmed-article:16018815 | lifeskim:mentions | umls-concept:C0733511 | lld:lifeskim |
| pubmed-article:16018815 | lifeskim:mentions | umls-concept:C0220888 | lld:lifeskim |
| pubmed-article:16018815 | lifeskim:mentions | umls-concept:C0037589 | lld:lifeskim |
| pubmed-article:16018815 | pubmed:dateCreated | 2005-8-4 | lld:pubmed |
| pubmed-article:16018815 | pubmed:abstractText | Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal. | lld:pubmed |
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| pubmed-article:16018815 | pubmed:language | eng | lld:pubmed |
| pubmed-article:16018815 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16018815 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:16018815 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16018815 | pubmed:issn | 1472-6947 | lld:pubmed |
| pubmed-article:16018815 | pubmed:author | pubmed-author:MandlKenneth... | lld:pubmed |
| pubmed-article:16018815 | pubmed:author | pubmed-author:OlsonKaren... | lld:pubmed |
| pubmed-article:16018815 | pubmed:author | pubmed-author:CassaChristop... | lld:pubmed |
| pubmed-article:16018815 | pubmed:author | pubmed-author:IancuKarinK | lld:pubmed |
| pubmed-article:16018815 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:16018815 | pubmed:volume | 5 | lld:pubmed |
| pubmed-article:16018815 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16018815 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16018815 | pubmed:pagination | 22 | lld:pubmed |
| pubmed-article:16018815 | pubmed:dateRevised | 2009-11-18 | lld:pubmed |
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| pubmed-article:16018815 | pubmed:meshHeading | pubmed-meshheading:16018815... | lld:pubmed |
| pubmed-article:16018815 | pubmed:year | 2005 | lld:pubmed |
| pubmed-article:16018815 | pubmed:articleTitle | A software tool for creating simulated outbreaks to benchmark surveillance systems. | lld:pubmed |
| pubmed-article:16018815 | pubmed:affiliation | Children's Hospital Informatics Program, Children's Hospital Boston, Boston, MA 02115, USA. cassa@mit.edu | lld:pubmed |
| pubmed-article:16018815 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16018815 | pubmed:publicationType | Research Support, U.S. Gov't, P.H.S. | lld:pubmed |
| pubmed-article:16018815 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:16018815 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
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