Título:
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A Quotient Basis Kernel for the prediction of mortality in severe sepsis patients
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Autor/a:
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Ribas Ripoll, Vicent; Romero Merino, Enrique; Ruiz Rodríguez, Juan Carlos; Vellido Alcacena, Alfredo
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing |
Abstract:
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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. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Intensive care units -Machine learning -Neural networks (Computer science) -Unitats de cures intensives -Aprenentatge automàtic -Xarxes neuronals (Informàtica) |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Objeto de conferencia |
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