<?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>0186-1042</journal-id>
<journal-title><![CDATA[Contaduría y administración]]></journal-title>
<abbrev-journal-title><![CDATA[Contad. Adm]]></abbrev-journal-title>
<issn>0186-1042</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Contaduría y Administración]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0186-10422023000300114</article-id>
<article-id pub-id-type="doi">10.22201/fca.24488410e.2023.4839</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Representación y gestión de datos de pacientes mexicanos con COVID-19 mediante grafos de conocimiento]]></article-title>
<article-title xml:lang="en"><![CDATA[Representation and management of data of Mexican patients with COVID-19 using knowledge graphs]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bravo]]></surname>
<given-names><![CDATA[Maricela]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[Darinel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Reyes-Ortiz]]></surname>
<given-names><![CDATA[José A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hoyos-Reyes]]></surname>
<given-names><![CDATA[Luis Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma Metropolitana  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2023</year>
</pub-date>
<volume>68</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0186-10422023000300114&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0186-10422023000300114&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0186-10422023000300114&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este artículo se presenta el desarrollo y evaluación de un grafo de conocimiento para la representación y gestión de datos de pacientes mexicanos que fueron registrados como casos positivos COVID-19. El empleo de grafos de conocimiento ofrece ventajas sobre otros modelos de representación, los grafos de conocimiento facilitan el acceso ágil a los datos mediante diversas tecnologías de consulta y acceso, lo que resulta relevante para la administración de sistemas de salud. Por otra parte, el empleo de grafos de conocimiento favorece la integración y expansión del conocimiento mediante la vinculación con otros grafos. En este trabajo se describe un enfoque de construcción que abarca desde el diseño del grafo general, la búsqueda y reutilización de grafos existentes, así como el registro de la información de pacientes COVID-19. Como resultado se obtuvo un modelo de grafo integral, eficiente y expandible; capaz de representar y extenderse incorporando información de otros grafos de conocimiento del dominio médico relacionados con los conceptos de la enfermedad del COVID-19, como son las vacunas, pruebas de laboratorio, los síntomas, medicamentos, así como como el diagnóstico clínico, lo que puede impactar en la mejora de la administración de sistemas de salud. El conjunto de datos utilizados durante las pruebas muestra la utilidad que tiene el grafo de conocimiento para registrar datos reales de pacientes mexicanos diagnosticados con COVID-19.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This paper presents the development and evaluation of a knowledge graph for the representation and data management from Mexican patients who were registered as positive COVID-19 cases. The use of knowledge graphs offers advantages over other representation models, knowledge graphs facilitate quick access to data through various query and access technologies, which is relevant for the management of health systems. On the other hand, the use of knowledge graphs favors the integration and expansion of knowledge by linking it with other graphs. This paper describes a construction approach that ranges from the design of the general graph, the search and reuse of existing graphs, as well as the registration of COVID-19 patient information. As a result, an integral, efficient and expandable graph model was obtained; capable of representing and extending by incorporating information from other knowledge graphs in the medical domain related to the concepts of the COVID-19 disease, such as vaccines, laboratory tests, symptoms, medications, as well as clinical diagnosis, which can impact on the improvement of the health systems management. The data set used during the tests shows the usefulness of the knowledge graph to record real data from Mexican patients diagnosed with COVID-19.]]></p></abstract>
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<kwd lng="es"><![CDATA[integración de grafos de conocimiento]]></kwd>
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<kwd lng="es"><![CDATA[grafos de conocimiento biomédico]]></kwd>
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<kwd lng="en"><![CDATA[biomedical knowledge graphs]]></kwd>
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</article-meta>
</front><back>
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