<?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>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322025000100106</article-id>
<article-id pub-id-type="doi">10.17488/rmib.46.1.3</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Synthetic data generation for pediatric diabetes research using GANs and WGANs]]></article-title>
<article-title xml:lang="es"><![CDATA[Generación de datos sintéticos para la investigación de la diabetes infantil usando GANs y WGANs]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-Domínguez]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Carlos E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Magallanes-Quintanar]]></surname>
<given-names><![CDATA[Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz-López]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vázquez-Moreno]]></surname>
<given-names><![CDATA[Miguel Alexander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Acosta-Cruz]]></surname>
<given-names><![CDATA[Erika]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Ingeniería Eléctrica ]]></institution>
<addr-line><![CDATA[ Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Centro Médico Nacional Siglo XXI  ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma de Coahuila Departamento de Biotecnología ]]></institution>
<addr-line><![CDATA[ Coahuila]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2025</year>
</pub-date>
<volume>46</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322025000100106&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322025000100106&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322025000100106&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Pediatric diabetes research is often constrained by data scarcity, hindering the development of accurate predictive models for clinical applications. This study addresses this limitation by evaluating the effectiveness of Generative Adversarial Networks (GANs) and Wasserstein GANs (WGANs) in generating synthetic datasets that replicate the statistical properties of real pediatric diabetes data. A structured methodology was applied, incorporating preprocessing, model design, and dual evaluation metrics: Jensen-Shannon and Kullback-Leibler divergences for statistical fidelity, and a classification model to assess practical utility. Results demonstrate that both models produce high-fidelity synthetic datasets, with WGANs showing superior performance in capturing complex patterns due to improved training stability. Nonetheless, challenges remain in replicating the inherent variability of pediatric data, influenced by growth and developmental factors. This work highlights the potential of synthetic data to augment pediatric diabetes datasets, facilitating the development of robust and generalizable predictive models. Limitations include the dependency on initial data quality and the specificity of the models to pediatric datasets. By addressing critical gaps in data availability, this study contributes to advancing AI-driven healthcare solutions in pediatric diabetes research.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La investigación en diabetes pediátrica a menudo está limitada por la escasez de datos, lo que dificulta el desarrollo de modelos predictivos precisos para aplicaciones clínicas. Este estudio aborda esta limitación evaluando la efectividad de las Redes Generativas Antagónicas (GANs) y las Wasserstein GANs (WGANs) para generar conjuntos de datos sintéticos que replican las propiedades estadísticas de los datos reales de diabetes pediátrica. Se aplicó una metodología estructurada que incluye el preprocesamiento, diseño de modelos y métricas de evaluación dual: divergencias de Jensen-Shannon y Kullback-Leibler para evaluar la fidelidad estadística, y un modelo de clasificación para evaluar la utilidad práctica. Los resultados demuestran que ambos modelos generan datos sintéticos de alta fidelidad, siendo las WGANs superiores en la captura de patrones complejos gracias a su estabilidad de entrenamiento mejorada. Sin embargo, persisten desafíos para replicar la variabilidad inherente de los datos pediátricos, influida por el crecimiento y los factores de desarrollo. Este trabajo resalta el potencial de los datos sintéticos para aumentar los conjuntos de datos de diabetes pediátrica, facilitando el desarrollo de modelos predictivos robustos y generalizables. Las limitaciones incluyen la dependencia de la calidad de los datos iniciales y la especificidad de los modelos a los conjuntos de datos pediátricos. Este estudio contribuye a cerrar brechas críticas en la disponibilidad de datos, impulsando soluciones de salud personalizadas basadas en inteligencia artificial para la investigación en diabetes pediátrica.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Generative Adversarial Networks (GANs)]]></kwd>
<kwd lng="en"><![CDATA[pediatric diabetes]]></kwd>
<kwd lng="en"><![CDATA[synthetic data generation]]></kwd>
<kwd lng="en"><![CDATA[Wasserstein GANs]]></kwd>
<kwd lng="es"><![CDATA[diabetes infantil]]></kwd>
<kwd lng="es"><![CDATA[generación de datos sintéticos]]></kwd>
<kwd lng="es"><![CDATA[Redes Generativas Adversarias (GANs)]]></kwd>
<kwd lng="es"><![CDATA[Redes Generativas Adversarias de Wasserstein (WGANs)]]></kwd>
</kwd-group>
</article-meta>
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