|
rdf:type |
|
|
lifeskim:mentions |
|
|
pubmed:issue |
10
|
|
pubmed:dateCreated |
2010-9-9
|
|
pubmed:abstractText |
The use of either symptom questionnaires or artificial neural networks (ANNs) has proven to improve the accuracy in diagnosing gastroesophageal reflux disease (GERD). However, the differentiation between the erosive and nonerosive reflux disease based upon symptoms at presentation still remains inconclusive.
|
|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:status |
MEDLINE
|
|
pubmed:month |
Oct
|
|
pubmed:issn |
1473-5687
|
|
pubmed:author |
|
|
pubmed:issnType |
Electronic
|
|
pubmed:volume |
22
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
1163-8
|
|
pubmed:meshHeading |
pubmed-meshheading:20526203-Adult,
pubmed-meshheading:20526203-Algorithms,
pubmed-meshheading:20526203-Diagnosis, Differential,
pubmed-meshheading:20526203-Duodenitis,
pubmed-meshheading:20526203-Female,
pubmed-meshheading:20526203-Gastroesophageal Reflux,
pubmed-meshheading:20526203-Humans,
pubmed-meshheading:20526203-Male,
pubmed-meshheading:20526203-Middle Aged,
pubmed-meshheading:20526203-Neural Networks (Computer),
pubmed-meshheading:20526203-Prospective Studies,
pubmed-meshheading:20526203-Questionnaires,
pubmed-meshheading:20526203-Sensitivity and Specificity,
pubmed-meshheading:20526203-Severity of Illness Index
|
|
pubmed:year |
2010
|
|
pubmed:articleTitle |
Is it possible to clinically differentiate erosive from nonerosive reflux disease patients? A study using an artificial neural networks-assisted algorithm.
|
|
pubmed:affiliation |
Division of Gastroenterology, Department of Clinical Sciences, L. Sacco University Hospital, Milano, Italy. fabio.pace@unimi.it
|
|
pubmed:publicationType |
Journal Article,
Validation Studies
|
