<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-29T17:03:10Z</responseDate><request verb="GetRecord" identifier="oai:repisalud.isciii.es:20.500.12105/23243" metadataPrefix="mets">https://repisalud.isciii.es/rest/oai/request</request><GetRecord><record><header><identifier>oai:repisalud.isciii.es:20.500.12105/23243</identifier><datestamp>2024-11-28T20:10:43Z</datestamp><setSpec>com_20.500.12105_15322</setSpec><setSpec>com_20.500.12105_2051</setSpec><setSpec>col_20.500.12105_16967</setSpec></header><metadata><mets xmlns="http://www.loc.gov/METS/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" ID="&#xa;&#x9;&#x9;&#x9;&#x9;DSpace_ITEM_20.500.12105-23243" TYPE="DSpace ITEM" PROFILE="DSpace METS SIP Profile 1.0" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd" OBJID="&#xa;&#x9;&#x9;&#x9;&#x9;hdl:20.500.12105/23243">
   <metsHdr CREATEDATE="2026-04-29T19:03:10Z">
      <agent ROLE="CUSTODIAN" TYPE="ORGANIZATION">
         <name>Repisalud</name>
      </agent>
   </metsHdr>
   <dmdSec ID="DMD_20.500.12105_23243">
      <mdWrap MDTYPE="MODS">
         <xmlData xmlns:mods="http://www.loc.gov/mods/v3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
            <mods:mods xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Mentis, Alexios-Fotios A</mods:namePart>
               </mods:name>
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Garcia, Irene</mods:namePart>
               </mods:name>
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Jimenez, Juan</mods:namePart>
               </mods:name>
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Paparoupa, Maria</mods:namePart>
               </mods:name>
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Xirogianni, Athanasia</mods:namePart>
               </mods:name>
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Papandreou, Anastasia</mods:namePart>
               </mods:name>
               <mods:name>
                  <mods:role>
                     <mods:roleTerm type="text">author</mods:roleTerm>
                  </mods:role>
                  <mods:namePart>Tzanakaki, Georgina</mods:namePart>
               </mods:name>
               <mods:extension>
                  <mods:dateAccessioned encoding="iso8601">2024-09-18T06:43:34Z</mods:dateAccessioned>
               </mods:extension>
               <mods:extension>
                  <mods:dateAvailable encoding="iso8601">2024-09-18T06:43:34Z</mods:dateAvailable>
               </mods:extension>
               <mods:originInfo>
                  <mods:dateIssued encoding="iso8601">2021-04</mods:dateIssued>
               </mods:originInfo>
               <mods:identifier type="citation">Mentis AFA, Garcia I, Jimenez J, Paparoupa M, Xirogianni A, Papandreou A, et al. Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study. Diagnostics. 2021 Apr;11(4):602.</mods:identifier>
               <mods:identifier type="doi">10.3390/diagnostics11040602</mods:identifier>
               <mods:identifier type="e-issn">2075-4418</mods:identifier>
               <mods:identifier type="journal">Diagnostics</mods:identifier>
               <mods:identifier type="other">https://hdl.handle.net/20.500.13003/19446</mods:identifier>
               <mods:identifier type="pubmedID">33800653</mods:identifier>
               <mods:identifier type="pui">L2007655223</mods:identifier>
               <mods:identifier type="scopus">2-s2.0-85109021139</mods:identifier>
               <mods:identifier type="uri">https://hdl.handle.net/20.500.12105/23243</mods:identifier>
               <mods:identifier type="wos">642969200001</mods:identifier>
               <mods:abstract>Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naive-Bayes (NB)) were applied for the two age groups (0-14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.</mods:abstract>
               <mods:language>
                  <mods:languageTerm authority="rfc3066">eng</mods:languageTerm>
               </mods:language>
               <mods:accessCondition type="useAndReproduction"/>
               <mods:subject>
                  <mods:topic>Meningitis</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Bacterial infection</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Viral infection</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Neutrophil-to-lymphocyte ratio</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Artificial intelligence</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Machine learning</mods:topic>
               </mods:subject>
               <mods:titleInfo>
                  <mods:title>Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study</mods:title>
               </mods:titleInfo>
               <mods:genre>research article</mods:genre>
            </mods:mods>
         </xmlData>
      </mdWrap>
   </dmdSec>
   <structMap LABEL="DSpace Object" TYPE="LOGICAL">
      <div TYPE="DSpace Object Contents" ADMID="DMD_20.500.12105_23243"/>
   </structMap>
</mets></metadata></record></GetRecord></OAI-PMH>