Linked Life Data

10699681

Source:http://linkedlifedata.com/resource/pubmed/id/10699681

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2000-6-6
pubmed:abstractText
The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0169-2607
pubmed:author
pubmed:issnType
Print
pubmed:volume
62
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
11-9
pubmed:dateRevised
2004-11-17
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Application of neural networks and sensitivity analysis to improved prediction of trauma survival.
pubmed:affiliation
Department of Computer Science, University of Sunderland, St Peter's Campus, Tyne and Wear, Sunderland, UK.
pubmed:publicationType
Journal Article