Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2007-12-10
pubmed:abstractText
The diagnosis of acute appendicitis is difficult, and a diagnostic error will often lead to either a perforation or the removal of a normal appendix. In this study, we constructed a Bayesian network model for the diagnosis of acute appendicitis and compared the diagnostic accuracy with other diagnostic models, such as the naive Bayes model, an artificial neural network model, and a logistic regression model.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0026-1270
pubmed:author
pubmed:issnType
Print
pubmed:volume
46
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
723-6
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Accuracy in the diagnostic prediction of acute appendicitis based on the Bayesian network model.
pubmed:affiliation
Division of Information Science and Biostatistics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
pubmed:publicationType
Journal Article, Evaluation Studies