Linked Life Data

12463839

Source:http://linkedlifedata.com/resource/pubmed/id/12463839

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2002-12-4
pubmed:abstractText
Despite the proven clinical benefits of HAART, mortality may still occur; particularly in those with less than 50 CD4+ cells/mL and, in some cases, with a viral burden below detectable plasma levels of HIV-1 RNA. Multiple factors may predict mortality including initial response to therapy, viral factors and host immune parameters. Due to the complexity of this problem, we developed Artificial Intelligence based tools/Neural Network (NN) to optimally evaluate outcomes of therapy and predict morbidity and mortality. To further validate the accuracy of these tools, we challenged their performance with that of Cox regression modeling (RM). Our study population involved 116 HIV+ individuals who consistently maintained CD4+ count < 50 cells/mL for over 6 months. All patients were treated with antiretrovirals. To assess clinical outcomes, we developed a feedforward back-propagation Neural Network. We then compared the performance of this network to a Cox regression model. The Neural Network outscored the Cox regression model in the ROC curve areas: 0.888 vs 0.760 (HIV+ first Seropositivity to AIDS), 0.901 vs 0.758 (HIV+ first Seropositivity to Last Assessment incl. death) and 0.832 vs 0.799 (AIDS to Last Assessment incl. death), for the NN & Cox, respectively. In patients with a history of AIDS defining events and with severe T-Cell depletion, mortality occurs despite therapy. Although Neural Networks and Cox modeling were successful in predicting mortality, the Neural Network was superior in assessing risk in this population.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1531-605X
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
320-4
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Neural networks morbidity and mortality modeling during loss of HIV T-cell homeostasis.
pubmed:affiliation
McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies