<?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>0034-8376</journal-id>
<journal-title><![CDATA[Revista de investigación clínica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. invest. clín.]]></abbrev-journal-title>
<issn>0034-8376</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0034-83762022000600314</article-id>
<article-id pub-id-type="doi">10.24875/ric.22000182</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villagrana-Bañuelos]]></surname>
<given-names><![CDATA[Karen E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Maeda-Gutiérrez]]></surname>
<given-names><![CDATA[Valeria]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alcalá-Rmz]]></surname>
<given-names><![CDATA[Vanessa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Oropeza-Valdez]]></surname>
<given-names><![CDATA[Juan J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Herrera-Van Oostdam]]></surname>
<given-names><![CDATA[Ana S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Castañeda-Delgado]]></surname>
<given-names><![CDATA[Julio E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López]]></surname>
<given-names><![CDATA[Jesús Adrián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Borrego Moreno]]></surname>
<given-names><![CDATA[Juan C.]]></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[Galván-Tejeda]]></surname>
<given-names><![CDATA[Jorge I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gamboa-Rosales]]></surname>
<given-names><![CDATA[Hamurabi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Luna-García]]></surname>
<given-names><![CDATA[Huizilopoztli]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Celaya-Padilla]]></surname>
<given-names><![CDATA[José M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Hernández]]></surname>
<given-names><![CDATA[Yamilé]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Electrical Engineering Academic Unit  ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Zacatecas  ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma de San Luis Potosí  ]]></institution>
<addr-line><![CDATA[SLP ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Mexicano de Seguridad Social  ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Autónoma de Zacatecas  ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,Instituto Mexicano del Seguro Social Hospital General de Zona 1 Emilio Varela Luján ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af7">
<institution><![CDATA[,Universidad Autónoma de Zacatecas  ]]></institution>
<addr-line><![CDATA[Zacatecas Zacatecas]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>74</volume>
<numero>6</numero>
<fpage>314</fpage>
<lpage>327</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0034-83762022000600314&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0034-83762022000600314&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0034-83762022000600314&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals.  Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university.  Methods: A total of 154 patients were included in the study. &#8220;Basic profile&#8221; was considered with clinical and demographic variables (33 variables), whereas in the &#8220;extended profile,&#8221; metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19.  Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables.  Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[Metabolomics]]></kwd>
<kwd lng="en"><![CDATA[Random forest]]></kwd>
<kwd lng="en"><![CDATA[Biomarker]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[Genetic algorithm]]></kwd>
<kwd lng="en"><![CDATA[LC-MS]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>1.</label><nlm-citation citation-type="book">
<collab>World Health Organization</collab>
<source><![CDATA[Coronavirus]]></source>
<year>2021</year>
<publisher-loc><![CDATA[Geneva ]]></publisher-loc>
<publisher-name><![CDATA[World Health Organization]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B2">
<label>2.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Marini]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Gattinoni]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Management of COVID-19 respiratory distress]]></article-title>
<source><![CDATA[JAMA.]]></source>
<year>2020</year>
<volume>323</volume>
<page-range>2329-30</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>3.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gao]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Cai]]></surname>
<given-names><![CDATA[GY]]></given-names>
</name>
<name>
<surname><![CDATA[Fang]]></surname>
<given-names><![CDATA[W]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[HY]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[SY]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning-based early warning system enables accurate mortality risk prediction for COVID-19]]></article-title>
<source><![CDATA[Nat Commun]]></source>
<year>2020</year>
<volume>11</volume>
<page-range>1-10</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>4.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[An]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Lim]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[DW]]></given-names>
</name>
<name>
<surname><![CDATA[Chang]]></surname>
<given-names><![CDATA[JH]]></given-names>
</name>
<name>
<surname><![CDATA[Choi]]></surname>
<given-names><![CDATA[YJ]]></given-names>
</name>
<name>
<surname><![CDATA[Kim]]></surname>
<given-names><![CDATA[SW]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study]]></article-title>
<source><![CDATA[Sci Rep.]]></source>
<year>2020</year>
<volume>10</volume>
<page-range>1-11</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>5.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Schwab]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Antholzer]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Haltmeier]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[NETT. solving inverse problems with deep neural networks]]></article-title>
<source><![CDATA[Inverse Probl.]]></source>
<year>2020</year>
<volume>36</volume>
<page-range>065005</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>6.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lassau]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Ammari]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Chouzenoux]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Gortais]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Herent]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Devilder]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients]]></article-title>
<source><![CDATA[Nat Commun]]></source>
<year>2021</year>
<volume>12</volume>
<page-range>634</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>7.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yan]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[HT]]></given-names>
</name>
<name>
<surname><![CDATA[Goncalves]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Xiao]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[An interpretable mortality prediction model for COVID-19 patients]]></article-title>
<source><![CDATA[Nat Mach Intell]]></source>
<year>2020</year>
<volume>2</volume>
<page-range>283-8</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>8.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Heldt]]></surname>
<given-names><![CDATA[FS]]></given-names>
</name>
<name>
<surname><![CDATA[Vizcaychipi]]></surname>
<given-names><![CDATA[MP]]></given-names>
</name>
<name>
<surname><![CDATA[Peacock]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
<name>
<surname><![CDATA[Cinelli]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[McLachlan]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Andreotti]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Early risk assessment for COVID-19 patients from emergency department data using machine learning]]></article-title>
<source><![CDATA[Sci Rep]]></source>
<year>2021</year>
<volume>11</volume>
<page-range>4200</page-range></nlm-citation>
</ref>
<ref id="B9">
<label>9.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Singh]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Kamaleswaran]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Chalfin]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Buño-Soto]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Roman]]></surname>
<given-names><![CDATA[JS]]></given-names>
</name>
<name>
<surname><![CDATA[Rojas-Kenney]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers]]></article-title>
<source><![CDATA[iScience]]></source>
<year>2021</year>
<volume>24</volume>
<page-range>103523</page-range></nlm-citation>
</ref>
<ref id="B10">
<label>10.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Quiroz-Juárez]]></surname>
<given-names><![CDATA[MA]]></given-names>
</name>
<name>
<surname><![CDATA[Torres-Gómez]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Hoyo-Ulloa]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[de J León-Montiel]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[U&#8217;Ren]]></surname>
<given-names><![CDATA[AB]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Identification of high-risk COVID-19 patients using machine learning]]></article-title>
<source><![CDATA[PLoS One]]></source>
<year>2021</year>
<volume>16</volume>
<page-range>e0257234</page-range></nlm-citation>
</ref>
<ref id="B11">
<label>11.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shen]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Yi]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Bi]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Du]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Proteomic and metabolomic characterization of COVID-19 patient sera]]></article-title>
<source><![CDATA[Cell]]></source>
<year>2020</year>
<volume>182</volume>
<page-range>59-72.e15</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>12.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sardar]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Sharma]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Gupta]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning assisted prediction of prognostic biomarkers associated with COVID-19, using clinical and proteomics data]]></article-title>
<source><![CDATA[Front Genet.]]></source>
<year>2021</year>
<volume>12</volume>
<page-range>636441</page-range></nlm-citation>
</ref>
<ref id="B13">
<label>13.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Celaya-Padilla]]></surname>
<given-names><![CDATA[JM]]></given-names>
</name>
<name>
<surname><![CDATA[Villagrana-Bañuelos]]></surname>
<given-names><![CDATA[KE]]></given-names>
</name>
<name>
<surname><![CDATA[Oropeza-Valdez]]></surname>
<given-names><![CDATA[JJ]]></given-names>
</name>
<name>
<surname><![CDATA[Monárrez-Espino]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Castañeda-Delgado]]></surname>
<given-names><![CDATA[JE]]></given-names>
</name>
<name>
<surname><![CDATA[Oostdam]]></surname>
<given-names><![CDATA[AS]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Kynurenine and hemoglobin as sex-specific variables in COVID-19 patients: a machine learning and genetic algorithms approach]]></article-title>
<source><![CDATA[Diagnostics]]></source>
<year>2021</year>
<volume>11</volume>
<page-range>2197</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>14.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Trevino]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Falciani]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Galgo: an r package for multivariate variable selection using genetic algorithms]]></article-title>
<source><![CDATA[Bioinformatics.]]></source>
<year>2006</year>
<volume>22</volume>
<page-range>1154-6</page-range></nlm-citation>
</ref>
<ref id="B15">
<label>15.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Breiman]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Random forests]]></article-title>
<source><![CDATA[Mach Learn.]]></source>
<year>2001</year>
<volume>45</volume>
<page-range>5-32</page-range></nlm-citation>
</ref>
<ref id="B16">
<label>16.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[García-Domínguez]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[CE]]></given-names>
</name>
<name>
<surname><![CDATA[Zanella-Calzada]]></surname>
<given-names><![CDATA[LA]]></given-names>
</name>
<name>
<surname><![CDATA[Gamboa-Rosales]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[JI]]></given-names>
</name>
<name>
<surname><![CDATA[Celaya-Padilla]]></surname>
<given-names><![CDATA[JM]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feature selection using genetic algorithms for the generation of a recognition and classification of children activities model using environmental sound]]></article-title>
<source><![CDATA[Mob Inf Syst]]></source>
<year>2020</year>
<volume>2020</volume>
<page-range>1-12</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>17.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sánchez-Reyna]]></surname>
<given-names><![CDATA[AG]]></given-names>
</name>
<name>
<surname><![CDATA[Celaya-Padilla]]></surname>
<given-names><![CDATA[JM]]></given-names>
</name>
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[CE]]></given-names>
</name>
<name>
<surname><![CDATA[Luna-García]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Gamboa-Rosales]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Ramírez-Morales]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multimodal early alzheimer&#8217;s detection, a genetic algorithm approach with support vector machines]]></article-title>
<source><![CDATA[Healthcare (Basel)]]></source>
<year>2021</year>
<volume>9</volume>
<page-range>971</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>18.</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Song]]></surname>
<given-names><![CDATA[F]]></given-names>
</name>
<name>
<surname><![CDATA[Guo]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Mei]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
</person-group>
<source><![CDATA[Feature Selection Using Principal Component Analysis. International Conference on System Science, Engineering Design and Manufacturing Informatization]]></source>
<year>2010</year>
<page-range>27-30</page-range><publisher-loc><![CDATA[Piscataway ]]></publisher-loc>
<publisher-name><![CDATA[IEEE]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>19.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fan]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Samworth]]></surname>
<given-names><![CDATA[R]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Ultrahigh dimensional feature selection: beyond the linear model]]></article-title>
<source><![CDATA[J Mach Learn Res.]]></source>
<year>2009</year>
<volume>10</volume>
<page-range>2013-38</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>20.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yue]]></surname>
<given-names><![CDATA[H]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[Z]]></given-names>
</name>
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study]]></article-title>
<source><![CDATA[Ann Transl Med]]></source>
<year>2020</year>
<volume>8</volume>
<page-range>859</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>21.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Schwab]]></surname>
<given-names><![CDATA[P]]></given-names>
</name>
<name>
<surname><![CDATA[Schütte]]></surname>
<given-names><![CDATA[AD]]></given-names>
</name>
<name>
<surname><![CDATA[Dietz]]></surname>
<given-names><![CDATA[B]]></given-names>
</name>
<name>
<surname><![CDATA[Bauer]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Clinical predictive models for COVID-19: systematic study]]></article-title>
<source><![CDATA[J Med Internet Res.]]></source>
<year>2020</year>
<volume>22</volume>
<page-range>e21439</page-range></nlm-citation>
</ref>
<ref id="B22">
<label>22.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Xiong]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Ruan]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Lu]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Comparing different machine learning techniques for predicting COVID-19 severity]]></article-title>
<source><![CDATA[Infect Dis Poverty]]></source>
<year>2022</year>
<volume>11</volume>
<page-range>19</page-range></nlm-citation>
</ref>
<ref id="B23">
<label>23.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[López-Hernández]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Monárrez-Espino]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Herrera-van Oostdam]]></surname>
<given-names><![CDATA[AS]]></given-names>
</name>
<name>
<surname><![CDATA[Delgado]]></surname>
<given-names><![CDATA[JE]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Targeted metabolomics identifies high performing diagnostic and prognostic biomarkers for COVID-19]]></article-title>
<source><![CDATA[Sci Rep]]></source>
<year>2021</year>
<volume>11</volume>
<page-range>14732</page-range></nlm-citation>
</ref>
<ref id="B24">
<label>24.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Herrera-Van Oostdam]]></surname>
<given-names><![CDATA[AS]]></given-names>
</name>
<name>
<surname><![CDATA[Castañeda-Delgado]]></surname>
<given-names><![CDATA[JE]]></given-names>
</name>
<name>
<surname><![CDATA[Oropeza-Valdez]]></surname>
<given-names><![CDATA[JJ]]></given-names>
</name>
<name>
<surname><![CDATA[Borrego]]></surname>
<given-names><![CDATA[JC]]></given-names>
</name>
<name>
<surname><![CDATA[Monárrez-Espino]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Zheng]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Immunometabolic signatures predict risk of progression to sepsis in COVID-19]]></article-title>
<source><![CDATA[PLoS One]]></source>
<year>2021</year>
<volume>16</volume>
<page-range>e0256784</page-range></nlm-citation>
</ref>
<ref id="B25">
<label>25.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Papoutsoglou]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Karaglani]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Lagani]]></surname>
<given-names><![CDATA[V]]></given-names>
</name>
<name>
<surname><![CDATA[Thomson]]></surname>
<given-names><![CDATA[N]]></given-names>
</name>
<name>
<surname><![CDATA[Røe]]></surname>
<given-names><![CDATA[OD]]></given-names>
</name>
<name>
<surname><![CDATA[Tsamardinos]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets]]></article-title>
<source><![CDATA[Sci Rep]]></source>
<year>2021</year>
<volume>11</volume>
<page-range>15107</page-range></nlm-citation>
</ref>
<ref id="B26">
<label>26.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Velasquez]]></surname>
<given-names><![CDATA[MT]]></given-names>
</name>
<name>
<surname><![CDATA[Ramezani]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Manal]]></surname>
<given-names><![CDATA[A]]></given-names>
</name>
<name>
<surname><![CDATA[Raj]]></surname>
<given-names><![CDATA[DS]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Trimethylamine N-oxide: the good, the bad and the unknown]]></article-title>
<source><![CDATA[Toxins (Basel).]]></source>
<year>2016</year>
<volume>8</volume>
<page-range>326</page-range></nlm-citation>
</ref>
<ref id="B27">
<label>27.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hochstrasser]]></surname>
<given-names><![CDATA[SR]]></given-names>
</name>
<name>
<surname><![CDATA[Metzger]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
<name>
<surname><![CDATA[Vincent]]></surname>
<given-names><![CDATA[AM]]></given-names>
</name>
<name>
<surname><![CDATA[Becker]]></surname>
<given-names><![CDATA[C]]></given-names>
</name>
<name>
<surname><![CDATA[Keller]]></surname>
<given-names><![CDATA[AK]]></given-names>
</name>
<name>
<surname><![CDATA[Beck]]></surname>
<given-names><![CDATA[K]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Trimethylamine-N-oxide (TMAO) predicts short- and long-term mortality and poor neurological outcome in out-of-hospital cardiac arrest patients]]></article-title>
<source><![CDATA[Clin Chem Lab Med]]></source>
<year>2021</year>
<volume>59</volume>
<page-range>393-402</page-range></nlm-citation>
</ref>
<ref id="B28">
<label>28.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dambrova]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Latkovskis]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
<name>
<surname><![CDATA[Kuka]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Strele]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Konrade]]></surname>
<given-names><![CDATA[I]]></given-names>
</name>
<name>
<surname><![CDATA[Grinberga]]></surname>
<given-names><![CDATA[S]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Diabetes is associated with higher trimethylamine N-oxide plasma levels]]></article-title>
<source><![CDATA[Exp Clin Endocrinol Diabetes]]></source>
<year>2016</year>
<volume>124</volume>
<page-range>251-6</page-range></nlm-citation>
</ref>
<ref id="B29">
<label>29.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Flores-Guerrero]]></surname>
<given-names><![CDATA[JL]]></given-names>
</name>
<name>
<surname><![CDATA[Osté]]></surname>
<given-names><![CDATA[MC]]></given-names>
</name>
<name>
<surname><![CDATA[Baraldi]]></surname>
<given-names><![CDATA[PB]]></given-names>
</name>
<name>
<surname><![CDATA[Connelly]]></surname>
<given-names><![CDATA[MA]]></given-names>
</name>
<name>
<surname><![CDATA[García]]></surname>
<given-names><![CDATA[E]]></given-names>
</name>
<name>
<surname><![CDATA[Navis]]></surname>
<given-names><![CDATA[G]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mo589 plasma concentrations of trimethylamine N-oxide, and its dietary determinants, are associated with increased risk of graft failure]]></article-title>
<source><![CDATA[Nephrol Dial Transplant.]]></source>
<year>2021</year>
<volume>36</volume>
<numero>Suppl 1</numero>
<issue>Suppl 1</issue>
<page-range>gfab089-2</page-range></nlm-citation>
</ref>
<ref id="B30">
<label>30.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sun]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Jiao]]></surname>
<given-names><![CDATA[X]]></given-names>
</name>
<name>
<surname><![CDATA[Ma]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[Y]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Trimethylamine N-oxide induces inflammation and endothelial dysfunction in human umbilical vein endothelial cells via activating ROS-txnip-nlrp3 inflammasome]]></article-title>
<source><![CDATA[Biochem Biophys Res Com]]></source>
<year>2016</year>
<volume>481</volume>
<page-range>63-70</page-range></nlm-citation>
</ref>
<ref id="B31">
<label>31.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[AP]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[JP]]></given-names>
</name>
<name>
<surname><![CDATA[Tao]]></surname>
<given-names><![CDATA[WQ]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[HM]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients]]></article-title>
<source><![CDATA[Int Immunopharmacol.]]></source>
<year>2020</year>
<volume>84</volume>
<page-range>106504</page-range></nlm-citation>
</ref>
<ref id="B32">
<label>32.</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[L]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[D]]></given-names>
</name>
<name>
<surname><![CDATA[Ding]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
<name>
<surname><![CDATA[Huang]]></surname>
<given-names><![CDATA[Q]]></given-names>
</name>
<name>
<surname><![CDATA[Tang]]></surname>
<given-names><![CDATA[YQ]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study]]></article-title>
<source><![CDATA[Signal Transduct Target Ther]]></source>
<year>2020</year>
<volume>5</volume>
<page-range>33</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
