<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0120-5633</journal-id>
<journal-title><![CDATA[Revista Colombiana de Cardiología]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Colomb. Cardiol.]]></abbrev-journal-title>
<issn>0120-5633</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Colombiana de Cardiologia. Oficina de Publicaciones]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-56332017000300255</article-id>
<article-id pub-id-type="doi">10.1016/j.rccar.2016.11.010</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Diagnóstico automático del síndrome coronario agudo utilizando un sistema multiagente basado en redes neuronales]]></article-title>
<article-title xml:lang="en"><![CDATA[Automatic diagnosis of acute coronary syndrome using a multi-agent system based in neural networks]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz]]></surname>
<given-names><![CDATA[John Jaime Sprockel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fernández]]></surname>
<given-names><![CDATA[Juan José Diaztagle]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guerrero]]></surname>
<given-names><![CDATA[Enrique González]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Fundación Universitaria de Ciencias de la Salud-Hospital de San José  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Colombia  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Pontificia Universidad Javeriana  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2017</year>
</pub-date>
<volume>24</volume>
<numero>3</numero>
<fpage>255</fpage>
<lpage>260</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-56332017000300255&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-56332017000300255&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-56332017000300255&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción:  Por tratarse de una tarea altamente compleja y de importancia clínica, el diagnóstico del síndrome coronario agudo se presta para su exploración por medio de modelado mediante sistemas inteligentes.  Objetivo:  desarrollar un sistema multiagente que ensamble las decisiones de varias redes neuronales para el diagnóstico del dolor torácico enfocado a los síndromes coronarios agudos.  Metodología:  estudio de pruebas diagnósticas en el que se entrenan un conjunto de redes neuronales con una precisión cercana al 70%, que luego son ensambladas mediante tres sistemas de votación para luego adicionar el resultado de redes especiales en poblaciones particulares y seleccionar la mejor configuración que hará parte de un sistema multiagente para el diagnóstico del dolor torácico.  Resultados:  Se generaron 84 redes con precisión promedio del 72% en pruebas; al ensamblarse aumentan dicha precisión hasta llegar a un máximo del 84% que tras la adición de los grupos especiales alcanza el 89%. Se escoge una conformación que brinda una sensibilidad del 96% con una especificidad del 77%, con valores predictivos positivo y negativo de 87 y 93% respectivamente para el diagnóstico de síndrome coronario agudo.  Conclusiones:  Es posible desarrollar una herramienta para el diagnóstico automático del síndrome coronario agudo a partir de un sistema multiagente que ensamble la disposición tomada por un conjunto de redes neuronales artificiales, cuyo rendimiento permite su consideración para su implementación dentro de un sistema de soporte a las decisiones clínicas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction:  Because it is a highly complex task of a great clinical importance, the diagnosis of acute coronary syndromes allows for their analysis by means of intelligent system models. Motivation: To develop a multi-agent system that assembles the decisions of several neural networks for the diagnosis of chest pain with a focus on acute coronary syndromes.  Methods:  A study of diagnostic tests where a series of neural networks are trained with a precision close to 70%, and are later on assembled with three voting systems. Then the results of special networks on specific populations are added to select the best configuration that Will make part of a multi-agent system for diagnosing chest pain.  Results:  A total of 84 networks were generated, with an average precision of 72% during testing; once assembled this precision rises up to a maximum of 84%, which then reaches 89% when the special groups are included. A configuration that offers a sensitivity of 96% with a specificity of 77% and positive and negative predictive values of 87 and 93% respectively is chosen for the diagnosis of acute coronary syndrome.  Conclusions:  It is possible to develop a tool for the automatic diagnosis of acute coronary syndrome using a multi-agent system that assembles the dispositions taken by a set of artificial neural networks. Its performance allows taking it into consideration for implementing it within a clinical decision-making support system.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Diagnóstico]]></kwd>
<kwd lng="es"><![CDATA[Síndrome coronario agudo]]></kwd>
<kwd lng="es"><![CDATA[Infarto agudo de miocardio]]></kwd>
<kwd lng="es"><![CDATA[Angina inestable]]></kwd>
<kwd lng="es"><![CDATA[Dolor torácico]]></kwd>
<kwd lng="en"><![CDATA[Diagnosis]]></kwd>
<kwd lng="en"><![CDATA[Acute coronary syndrome]]></kwd>
<kwd lng="en"><![CDATA[Acute myocardial infarction]]></kwd>
<kwd lng="en"><![CDATA[Unstable angina]]></kwd>
<kwd lng="en"><![CDATA[Chest pain]]></kwd>
</kwd-group>
</article-meta>
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