<?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>0864-084X</journal-id>
<journal-title><![CDATA[Nucleus]]></journal-title>
<abbrev-journal-title><![CDATA[Nucleus]]></abbrev-journal-title>
<issn>0864-084X</issn>
<publisher>
<publisher-name><![CDATA[CUBAENERGIA]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0864-084X2019000100006</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Development of clinically based prediction models using machine learning and Bayesian statistics]]></article-title>
<article-title xml:lang="es"><![CDATA[Desarrollo de modelos de predicción basados clínicamente utilizando aprendizaje automático y estadísticas bayesianas.]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zambrano Ramírez]]></surname>
<given-names><![CDATA[Oscar Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[FONTBONNE]]></surname>
<given-names><![CDATA[Jean-Marc]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Université Caen Normandie Laboratoire de Physique Corpusculaire (LPC-Caen) ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>France</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2019</year>
</pub-date>
<numero>65</numero>
<fpage>6</fpage>
<lpage>10</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S0864-084X2019000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S0864-084X2019000100006&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S0864-084X2019000100006&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this work, the framework for developing generic clinically based models is emphasized and illustrated with Bayesian statistics neurologic grade prediction models in order to exemplify the type of models that can be developed from a mathematical point of view. The models are based on clinical records of patients who underwent radiotherapy treatment due to glioblastoma which is an aggressive brain cancer. A first model requires as a parameter the neurologic grade of the patient before the treatment then predicts the grade after the treatment. A second, enhanced, model was developed with the aim of making the prediction more realistic and it uses the neurologic grade before the treatment as well, but it additionally depends on the Clinical Target Volume (CTV). Furthermore, with the aid of Bayesian statistic we were able to estimate the uncertainty of the predictions.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este trabajo el marco teórico, para desarrollar modelos genéricos basados en datos clínicos, se enfatiza e ilustra con estadísticas bayesianas las cuales predicen grados neurológicos para ilustrar los tipos de modelos que se pueden desarrollar desde un punto de vista matemático. Los modelos se basan en datos clínicos de pacientes que se han sometido a radioterapia por causa de un glioblastoma, el cual es un cáncer de cerebro agresivo. Un primer modelo requiere como parámetro el grado neurológico del paciente antes del tratamiento y predice el grado después del tratamiento. Un segundo modelo, mejorado, fue desarrollado con el propósito de hacerlo más real, éste emplea también el grado neurológico antes del tratamiento; además depende del Volumen Blanco Clínico (CTV por sus siglas en inglés). Por último, con el uso de estadísticas bayesianas fue posible estimar la incertidumbre de las predicciones.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[learning]]></kwd>
<kwd lng="en"><![CDATA[adaptive systems]]></kwd>
<kwd lng="en"><![CDATA[statistics]]></kwd>
<kwd lng="en"><![CDATA[clinical trials]]></kwd>
<kwd lng="en"><![CDATA[prediction equations]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje]]></kwd>
<kwd lng="es"><![CDATA[sistemas adaptivos]]></kwd>
<kwd lng="es"><![CDATA[estadística]]></kwd>
<kwd lng="es"><![CDATA[ensayos clínicos]]></kwd>
<kwd lng="es"><![CDATA[ecuaciones de predicción]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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