<?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>0864-3466</journal-id>
<journal-title><![CDATA[Revista Cubana de Salud Pública]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Cubana Salud Pública]]></abbrev-journal-title>
<issn>0864-3466</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ciencias Médicas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0864-34662020000400010</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Sistema de apoyo al diagnóstico médico de COVID-19 mediante mapa cognitivo difuso]]></article-title>
<article-title xml:lang="en"><![CDATA[Support system to Covid-19 medical diagnosis via diffuse cognitive map]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mar Cornelio]]></surname>
<given-names><![CDATA[Omar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gulín González]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bron Fonseca]]></surname>
<given-names><![CDATA[Barbara]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Garcés Espinosa]]></surname>
<given-names><![CDATA[Jeovanys Víctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de las Ciencias Informáticas Centro de Estudio de Matemática Computacional ]]></institution>
<addr-line><![CDATA[La Habana ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de las Ciencias Informáticas Facultad de Ciencias y Tecnologías Computacionales ]]></institution>
<addr-line><![CDATA[La Habana ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Policlínico Docente &#8220;Ernesto Guevara&#8221;  ]]></institution>
<addr-line><![CDATA[Granma ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>46</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S0864-34662020000400010&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S0864-34662020000400010&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S0864-34662020000400010&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción: Los escenarios de convivencia de las diversas poblaciones son muy complejos, lo que contribuye con la propagación de enfermedades. Diagnosticar tempranamente enfermedades infecciosas representa una tarea fundamental para disminuir su propagación y evitar epidemias. Sin embargo, la inconsistencia en los datos de poblaciones y la imposibilidad de contar con un diagnóstico oportuno en muchos casos trae como consecuencia la proliferación de pandemias tales como la COVID-19.  Objetivo: Desarrollar un sistema de apoyo al diagnóstico médico para COVID-19 a partir de la modelación de las relaciones causales de los criterios de diagnóstico, para conformar el mapa cognitivo difuso.  Métodos: Para el desarrollo de la investigación se utilizaron métodos teóricos, empíricos y estadísticos, tales como: analítico-sintético, inductivo-deductivo, hipotético-deductivo, modelación. Como método empírico se utilizó la entrevista semiestructurada con la intención de recoger información que permitiera incluir contenidos no prescritos y precisar el conocimiento de los expertos sobre los principales indicadores para la toma de decisiones en el diagnóstico médico de la COVID-19.  Resultados: El sistema funciona a través de un mapa cognitivo difuso para modelar las relaciones causales que representan la base de la inferencia. Se utilizan técnicas de inteligencia artificial como base al diagnóstico médico. Se presenta un ejemplo demostrativo para el diagnóstico médico de la COVID-19 en el que se modelan las relaciones causales de los diferentes conceptos que describen la enfermedad provocada.  Conclusiones: El sistema diseñado constituye una herramienta viable de apoyo a la toma de decisiones en el diagnóstico médico de la COVID-19, que permite obtener criterios evaluativos a partir de la modelación de las relaciones causales, esto lo hace extensible a otros tipos de situaciones de emergencias sanitarias.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  Different populations coexistence scenarios are very complex, which contributes to the spread of diseases. Diagnosing infectious diseases early is a critical task in reducing its spread and preventing epidemics. However, inconsistency in population data and the inability to have timely diagnosis in many cases result in the proliferation of pandemics such as COVID-19.  Objective:  Develop a support system for COVID-19 medical diagnostic from modeling causal relations of diagnostic criteria, to form the diffuse cognitive map.  Methods:  Theoretical, empirical and statistical methods were used for the development of the research, such as: analytical-synthetic, inductive-deductive, hypothetical-deductive, modeling. As an empirical method, the semi-structured interview was used with the intention of collecting information that would include unprescribed contents and require expert knowledge of the main indicators for decision-making in COVID-19 medical diagnosis.  Results:  The system works through a diffuse cognitive map to model causal relationships that represent the inference´s basis. Artificial intelligence techniques are used as a basis for medical diagnosis. A demonstrative example is presented for COVID-19 medical diagnosis in which are modelled the causal relations of the different concepts that the disease describes.  Conclusions:  The designed system is a viable support tool for decision-making in COVID-19 medical diagnosis, which allows to obtain evaluative criteria from the modelling of causal relations, and this makes it extendable to other types of health emergencies situations.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[diagnóstico médico]]></kwd>
<kwd lng="es"><![CDATA[enfermedades infecciosas]]></kwd>
<kwd lng="es"><![CDATA[mapa cognitivo difuso]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[medical diagnosis]]></kwd>
<kwd lng="en"><![CDATA[infectious diseases]]></kwd>
<kwd lng="en"><![CDATA[diffuse cognitive map]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
</kwd-group>
</article-meta>
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