<?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>2011-7582</journal-id>
<journal-title><![CDATA[Revista Colombiana de Cirugía]]></journal-title>
<abbrev-journal-title><![CDATA[rev. colomb. cir.]]></abbrev-journal-title>
<issn>2011-7582</issn>
<publisher>
<publisher-name><![CDATA[Asociación Colombiana de Cirugía]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2011-75822023000300439</article-id>
<article-id pub-id-type="doi">10.30944/20117582.2225</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Red neural artificial para predecir factores de riesgo asociados a complicaciones postoperatorias secundarias al tratamiento del neumotórax]]></article-title>
<article-title xml:lang="en"><![CDATA[Artificial neural network to predict risk factors associated with postoperative complications secondary to pneumothorax treatment]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Domínguez]]></surname>
<given-names><![CDATA[Saturnino]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Andrade-Alegre]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Hospital Santo Tomás Servicio de Cirugía General ]]></institution>
<addr-line><![CDATA[Ciudad de Panamá ]]></addr-line>
<country>Panamá</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Hospital Santo Tomás Departamento de Cirugía ]]></institution>
<addr-line><![CDATA[Ciudad de Panamá ]]></addr-line>
<country>Panamá</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>38</volume>
<numero>3</numero>
<fpage>439</fpage>
<lpage>446</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S2011-75822023000300439&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S2011-75822023000300439&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S2011-75822023000300439&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción.  Debido a la ausencia de modelos predictivos estadísticamente significativos enfocados a las complicaciones postoperatorias en el manejo quirúrgico del neumotórax, desarrollamos un modelo, utilizando redes neurales, que identifica las variables independientes y su importancia para reducir la incidencia de complicaciones.  Métodos.  Se realizó un estudio retrospectivo en un centro asistencial, donde se incluyeron 106 pacientes que requirieron manejo quirúrgico de neumotórax. Todos fueron operados por el mismo cirujano. Se desarrolló una red neural artificial para manejo de datos con muestras limitadas; se optimizaron los datos y cada algoritmo fue evaluado de forma independiente y mediante validación cruzada, para obtener el menor error posible y la mayor precisión con el menor tiempo de respuesta.  Resultados.  Las variables de mayor importancia según su peso en el sistema de decisión de la red neural (área bajo la curva 0,991) fueron el abordaje por toracoscopia video asistida (OR 1,131), el uso de pleurodesis con talco (OR 0,994) y el uso de autosuturas (OR 0,792; p&lt;0,05).  Discusión.  En nuestro estudio, los principales predictores independientes asociados a mayor riesgo de complicaciones fueron el neumotórax de etiología secundaria y el neumotórax recurrente. Adicionalmente, confirmamos que las variables asociadas a reducción de riesgo de complicaciones postoperatorias tuvieron significancia estadística.  Conclusión.  Identificamos la toracoscopia video asistida, el uso de autosuturas y la pleurodesis con talco como posibles variables asociadas a menor riesgo de complicaciones. Se plantea la posibilidad de desarrollar una herramienta que facilite y apoye la toma de decisiones, por lo cual es necesaria la validación externa en estudios prospectivos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction.  Due to the absence of statistically significant predictive models focused on postoperative complications in the surgical management of pneumothorax, we developed a model using neural networks that identify the independent variables and their importance in reducing the incidence of postoperative complications.  Methods.  A retrospective single-center study was carried out, where 106 patients who required surgical management of pneumothorax were included. All patients were operated by the same surgeon. An artificial neural network was developed to manage data with limited samples. The data is optimized and each algorithm is evaluated independently and through cross-validation to obtain the lowest possible error and the highest precision with the shortest response time.  Results.  The most important variables according to their weight in the decision system of the neural network (AUC 0.991) were the approach via video-assisted thoracoscopy (OR 1.131), use of pleurodesis with powder talcum (OR 0.994) and use of autosutures (OR 0.792, p&lt;0.05).  Discussion.  In our study, the main independent predictors associated with a higher risk of complications are pneumothorax of secondary etiology and recurrent pneumothorax. Additionally, we confirm that the variables associated with a reduction in the risk of postoperative complications have statistical significance.  Conclusion.  We identify video-assisted thoracoscopy, use of autosuture and powder talcum pleurodesis as possible variables associated with a lower risk of complications and raise the possibility of developing a tool that facilitates and supports decision-making, for which external validation in prospective studies is necessary.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[redes neurales de la computación]]></kwd>
<kwd lng="es"><![CDATA[neumotórax]]></kwd>
<kwd lng="es"><![CDATA[toracoscopía]]></kwd>
<kwd lng="es"><![CDATA[talco]]></kwd>
<kwd lng="es"><![CDATA[complicaciones posoperatorias]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[computer neural networks]]></kwd>
<kwd lng="en"><![CDATA[pneumothorax]]></kwd>
<kwd lng="en"><![CDATA[thoracoscopy]]></kwd>
<kwd lng="en"><![CDATA[powder talcum]]></kwd>
<kwd lng="en"><![CDATA[postoperative complications]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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