Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2005-3-18
pubmed:abstractText
Neural networks are new methodological tools based on nonlinear models. They appear to be better at prediction and classification in biological systems than do traditional strategies such as logistic regression. This paper provides a practical example that contrasts both approaches within the setting of suspected sepsis in the emergency room.
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-10075620, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-11246308, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-12700374, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-14727015, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-14985955, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-15191321, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-15200449, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-15262061, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-15279549, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-15774070, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-1597042, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-1738016, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-7063747, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-7799491, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-8417638, http://linkedlifedata.com/resource/pubmed/commentcorrection/15774048-8892489
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1466-609X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
9
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
R150-6
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed-meshheading:15774048-Adolescent, pubmed-meshheading:15774048-Adult, pubmed-meshheading:15774048-Algorithms, pubmed-meshheading:15774048-Bacterial Infections, pubmed-meshheading:15774048-Cohort Studies, pubmed-meshheading:15774048-Emergency Service, Hospital, pubmed-meshheading:15774048-Follow-Up Studies, pubmed-meshheading:15774048-Glasgow Coma Scale, pubmed-meshheading:15774048-Humans, pubmed-meshheading:15774048-Intensive Care Units, pubmed-meshheading:15774048-Length of Stay, pubmed-meshheading:15774048-Logistic Models, pubmed-meshheading:15774048-Longitudinal Studies, pubmed-meshheading:15774048-Middle Aged, pubmed-meshheading:15774048-Neural Networks (Computer), pubmed-meshheading:15774048-Prognosis, pubmed-meshheading:15774048-ROC Curve, pubmed-meshheading:15774048-Risk Assessment, pubmed-meshheading:15774048-Risk Factors, pubmed-meshheading:15774048-Sepsis, pubmed-meshheading:15774048-Time Factors
pubmed:year
2005
pubmed:articleTitle
Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room.
pubmed:affiliation
Department of Internal Medicine, Escuela de Investigaciones Médicas Aplicadas (EIMA - GRAEPI), School of Medicine, Universidad de Antioquia, Medellín, Colombia. fjaimes@catios.udea.edu.co
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't, Evaluation Studies