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Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study
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 naïve-Bayes (NB)) were applied for the two age groups (0–14 and...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065596/ https://www.ncbi.nlm.nih.gov/pubmed/33800653 http://dx.doi.org/10.3390/diagnostics11040602 |
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author | Mentis, Alexios-Fotios A. Garcia, Irene Jiménez, Juan Paparoupa, Maria Xirogianni, Athanasia Papandreou, Anastasia Tzanakaki, Georgina |
author_facet | Mentis, Alexios-Fotios A. Garcia, Irene Jiménez, Juan Paparoupa, Maria Xirogianni, Athanasia Papandreou, Anastasia Tzanakaki, Georgina |
author_sort | Mentis, Alexios-Fotios A. |
collection | PubMed |
description | 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 naïve-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. |
format | Online Article Text |
id | pubmed-8065596 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80655962021-04-25 Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study Mentis, Alexios-Fotios A. Garcia, Irene Jiménez, Juan Paparoupa, Maria Xirogianni, Athanasia Papandreou, Anastasia Tzanakaki, Georgina Diagnostics (Basel) Article 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 naïve-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. MDPI 2021-03-28 /pmc/articles/PMC8065596/ /pubmed/33800653 http://dx.doi.org/10.3390/diagnostics11040602 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Mentis, Alexios-Fotios A. Garcia, Irene Jiménez, Juan Paparoupa, Maria Xirogianni, Athanasia Papandreou, Anastasia Tzanakaki, Georgina Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study |
title | Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study |
title_full | Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study |
title_fullStr | Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study |
title_full_unstemmed | Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study |
title_short | Artificial Intelligence in Differential Diagnostics of Meningitis: A Nationwide Study |
title_sort | artificial intelligence in differential diagnostics of meningitis: a nationwide study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8065596/ https://www.ncbi.nlm.nih.gov/pubmed/33800653 http://dx.doi.org/10.3390/diagnostics11040602 |
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