<?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>1316-4821</journal-id>
<journal-title><![CDATA[Universidad, Ciencia y Tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[uct]]></abbrev-journal-title>
<issn>1316-4821</issn>
<publisher>
<publisher-name><![CDATA[AutanaBooks S.A.S. Revista de la Universidad Experimental Politécnica Antonio José de Sucre, Vice Rectorado Puerto Ordaz, Venezuela, gestionada en Ecuador por AutanaBooks]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1316-48212023000300019</article-id>
<article-id pub-id-type="doi">10.47460/uct.v27i120.728</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Predicción del ausentismo en citas médicas mediante Machine Learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Prediction of absenteeism in medical appointments by Machine Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valenzuela-Núñez]]></surname>
<given-names><![CDATA[Catalina Isabel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Troncoso Espinosa]]></surname>
<given-names><![CDATA[Fredy Humberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Latorre-Núñez]]></surname>
<given-names><![CDATA[Guillermo Octavio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Bío-Bío Facultad de Ingeniería Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[Concepción ]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad del Bío-Bío Facultad de Ingeniería Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[Concepción ]]></addr-line>
<country>Chile</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad del Bío-Bío Facultad de Ingeniería Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[Concepción ]]></addr-line>
<country>Chile</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>27</volume>
<numero>120</numero>
<fpage>19</fpage>
<lpage>30</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S1316-48212023000300019&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S1316-48212023000300019&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S1316-48212023000300019&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen. La programación de citas médicas es una actividad de gran importancia en un hospital, ya que se deben utilizar de forma eficiente diferentes capitales, tanto humanos como materiales. Uno de los problemas de este trabajo es la inasistencia de un paciente, lo que disminuye la eficiencia del uso de estos recursos. Para hacer frente a esto, diversos estudios han propuesto considerar el ausentismo para programar las citas médicas. Sin embargo, predecirlo es una tarea compleja. Esta investigación propone la predicción de la no asistencia a la citación para tres áreas médicas del Hospital Clínico Regional Dr. Guillermo Grant Benavente en la ciudad de Concepción, Chile. Para esto se entrenan y evalúan cinco algoritmos de Machine Learning. El mejor modelo entrenado logró ser una herramienta predictiva del nivel de ausentismo de un paciente para su próxima consulta y caracterizar a aquellos pacientes con mayores niveles de ausentismo.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract. The scheduling of medical appointments is an activity of great importance in a hospital, since different capitals, both human and material, must be used efficiently. One of the problems of this work is the non-attendance of a patient, which decreases the efficiency of the use of resources. To address this, several studies have proposed considering "absenteeism" for scheduling medical appointments. However, predicting it is a complex task. This research proposes the prediction of absenteeism to medical appointments for three medical areas of the Hospital Clínico Regional Dr. Guillermo Grant Benavente in the city of Concepción, Chile. For this purpose, five Machine Learning algorithms are trained and evaluated. The best trained model managed to be a predictive tool of the level of absenteeism of a patient for his next appointment and to characterize those patients with higher levels of no-show.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Machine Learning]]></kwd>
<kwd lng="es"><![CDATA[Citas Médicas]]></kwd>
<kwd lng="es"><![CDATA[Ausentismo]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[Medical Appointments]]></kwd>
<kwd lng="en"><![CDATA[Absenteeism]]></kwd>
</kwd-group>
</article-meta>
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