|
rdf:type |
|
|
lifeskim:mentions |
umls-concept:C0013018,
umls-concept:C0018133,
umls-concept:C0030705,
umls-concept:C0039730,
umls-concept:C0205178,
umls-concept:C0206031,
umls-concept:C0445356,
umls-concept:C0472699,
umls-concept:C0598941,
umls-concept:C1707455,
umls-concept:C2004457
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|
pubmed:issue |
5
|
|
pubmed:dateCreated |
2010-4-13
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|
pubmed:abstractText |
There is growing interest in the development of prognostic models for predicting the occurrence of acute graft-vs-host disease (aGVHD) after unrelated donor hematopoietic stem cell transplantation. A high number of variables have been shown to play a role in aGVHD, but the search for a predictive algorithm is still ongoing. Artificial neural networks (ANNs) represent an attractive alternative to multivariate analysis for clinical prognosis. So far, no reports have investigated the ability of ANNs in predicting HSCT outcome.
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|
pubmed:language |
eng
|
|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:chemical |
|
|
pubmed:status |
MEDLINE
|
|
pubmed:month |
May
|
|
pubmed:issn |
1873-2399
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|
pubmed:author |
|
|
pubmed:copyrightInfo |
2010 ISEH-Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.
|
|
pubmed:issnType |
Electronic
|
|
pubmed:volume |
38
|
|
pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
|
|
pubmed:pagination |
426-33
|
|
pubmed:dateRevised |
2010-11-18
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|
pubmed:meshHeading |
pubmed-meshheading:20206661-Acute Disease,
pubmed-meshheading:20206661-Adolescent,
pubmed-meshheading:20206661-Adult,
pubmed-meshheading:20206661-Child,
pubmed-meshheading:20206661-Child, Preschool,
pubmed-meshheading:20206661-Female,
pubmed-meshheading:20206661-Graft vs Host Disease,
pubmed-meshheading:20206661-HLA Antigens,
pubmed-meshheading:20206661-Haplotypes,
pubmed-meshheading:20206661-Hematopoietic Stem Cell Transplantation,
pubmed-meshheading:20206661-Humans,
pubmed-meshheading:20206661-Infant,
pubmed-meshheading:20206661-Kaplan-Meier Estimate,
pubmed-meshheading:20206661-Living Donors,
pubmed-meshheading:20206661-Logistic Models,
pubmed-meshheading:20206661-Male,
pubmed-meshheading:20206661-Middle Aged,
pubmed-meshheading:20206661-Neural Networks (Computer),
pubmed-meshheading:20206661-Prognosis,
pubmed-meshheading:20206661-Random Allocation,
pubmed-meshheading:20206661-Receptors, KIR,
pubmed-meshheading:20206661-Survival Analysis,
pubmed-meshheading:20206661-Transplantation Conditioning,
pubmed-meshheading:20206661-Treatment Outcome,
pubmed-meshheading:20206661-Young Adult,
pubmed-meshheading:20206661-beta-Thalassemia
|
|
pubmed:year |
2010
|
|
pubmed:articleTitle |
Comparison between an artificial neural network and logistic regression in predicting acute graft-vs-host disease after unrelated donor hematopoietic stem cell transplantation in thalassemia patients.
|
|
pubmed:affiliation |
Dipartimento di Scienze Mediche Internistiche, Centro Trapianti Midollo Osseo, Università di Cagliari, Cagliari, Italy. gcaocci@alice.it
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|
pubmed:publicationType |
Journal Article,
Comparative Study,
Evaluation Studies
|
