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A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients
dc.contributor.author | Ribas Ripoll, Vicent |
dc.contributor.author | Romero Merino, Enrique |
dc.contributor.author | Ruiz Rodríguez, Juan Carlos |
dc.contributor.author | Vellido Alcacena, Alfredo |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2014-06-20T08:33:54Z |
dc.date.available | 2014-06-20T08:33:54Z |
dc.date.created | 2013 |
dc.date.issued | 2013 |
dc.identifier.citation | Ribas, V. [et al.]. A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24-26 April 2013". Bruges: 2013, p. 379-384. |
dc.identifier.isbn | 978-2-87419-081-0 |
dc.identifier.uri | http://hdl.handle.net/2117/23280 |
dc.description.abstract | In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a common clinical syndrome, often treated at the Intensive Care Unit (ICU) in a time-critical context. Mortality prediction results with Support Vector Machines using QBK compare favorably with those obtained using alternative kernels and standard clinical procedures. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Intensive care units |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Neural networks (Computer science) |
dc.title | A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients |
dc.type | Conference report |
dc.subject.lemac | Unitats de cures intensives |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Xarxes neuronals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. SOCO - Soft Computing |
dc.relation.publisherversion | https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2013 |
dc.rights.access | Open Access |
local.identifier.drac | 12908734 |
dc.description.version | Postprint (published version) |
local.citation.author | Ribas, V.; Romero, E.; Ruiz-Rodríguez, Juan C.; Vellido, A. |
local.citation.contributor | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
local.citation.pubplace | Bruges |
local.citation.publicationName | ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24-26 April 2013 |
local.citation.startingPage | 379 |
local.citation.endingPage | 384 |