Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach

Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent softwar...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Dejan Dobrijević [verfasserIn] Jelena Antić [verfasserIn] Goran Rakić [verfasserIn] Jasmina Katanić [verfasserIn] Ljiljana Andrijević [verfasserIn] Kristian Pastor [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning

Übergeordnetes Werk:	In: Children - MDPI AG, 2014, 10(2023), 5, p 761
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:5, p 761

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/children10050761

Katalog-ID:	DOAJ094401748

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520			\|a Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.
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spelling	10.3390/children10050761 doi (DE-627)DOAJ094401748 (DE-599)DOAJ5676640497a54e259a49b014a4991dd7 DE-627 ger DE-627 rakwb eng RJ1-570 Dejan Dobrijević verfasserin aut Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning Pediatrics Jelena Antić verfasserin aut Goran Rakić verfasserin aut Jasmina Katanić verfasserin aut Ljiljana Andrijević verfasserin aut Kristian Pastor verfasserin aut In Children MDPI AG, 2014 10(2023), 5, p 761 (DE-627)768093007 (DE-600)2732685-8 22279067 nnns volume:10 year:2023 number:5, p 761 https://doi.org/10.3390/children10050761 kostenfrei https://doaj.org/article/5676640497a54e259a49b014a4991dd7 kostenfrei https://www.mdpi.com/2227-9067/10/5/761 kostenfrei https://doaj.org/toc/2227-9067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 5, p 761
allfields_unstemmed	10.3390/children10050761 doi (DE-627)DOAJ094401748 (DE-599)DOAJ5676640497a54e259a49b014a4991dd7 DE-627 ger DE-627 rakwb eng RJ1-570 Dejan Dobrijević verfasserin aut Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning Pediatrics Jelena Antić verfasserin aut Goran Rakić verfasserin aut Jasmina Katanić verfasserin aut Ljiljana Andrijević verfasserin aut Kristian Pastor verfasserin aut In Children MDPI AG, 2014 10(2023), 5, p 761 (DE-627)768093007 (DE-600)2732685-8 22279067 nnns volume:10 year:2023 number:5, p 761 https://doi.org/10.3390/children10050761 kostenfrei https://doaj.org/article/5676640497a54e259a49b014a4991dd7 kostenfrei https://www.mdpi.com/2227-9067/10/5/761 kostenfrei https://doaj.org/toc/2227-9067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 5, p 761
allfieldsGer	10.3390/children10050761 doi (DE-627)DOAJ094401748 (DE-599)DOAJ5676640497a54e259a49b014a4991dd7 DE-627 ger DE-627 rakwb eng RJ1-570 Dejan Dobrijević verfasserin aut Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning Pediatrics Jelena Antić verfasserin aut Goran Rakić verfasserin aut Jasmina Katanić verfasserin aut Ljiljana Andrijević verfasserin aut Kristian Pastor verfasserin aut In Children MDPI AG, 2014 10(2023), 5, p 761 (DE-627)768093007 (DE-600)2732685-8 22279067 nnns volume:10 year:2023 number:5, p 761 https://doi.org/10.3390/children10050761 kostenfrei https://doaj.org/article/5676640497a54e259a49b014a4991dd7 kostenfrei https://www.mdpi.com/2227-9067/10/5/761 kostenfrei https://doaj.org/toc/2227-9067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 5, p 761
allfieldsSound	10.3390/children10050761 doi (DE-627)DOAJ094401748 (DE-599)DOAJ5676640497a54e259a49b014a4991dd7 DE-627 ger DE-627 rakwb eng RJ1-570 Dejan Dobrijević verfasserin aut Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children. COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning Pediatrics Jelena Antić verfasserin aut Goran Rakić verfasserin aut Jasmina Katanić verfasserin aut Ljiljana Andrijević verfasserin aut Kristian Pastor verfasserin aut In Children MDPI AG, 2014 10(2023), 5, p 761 (DE-627)768093007 (DE-600)2732685-8 22279067 nnns volume:10 year:2023 number:5, p 761 https://doi.org/10.3390/children10050761 kostenfrei https://doaj.org/article/5676640497a54e259a49b014a4991dd7 kostenfrei https://www.mdpi.com/2227-9067/10/5/761 kostenfrei https://doaj.org/toc/2227-9067 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2023 5, p 761
language	English
source	In Children 10(2023), 5, p 761 volume:10 year:2023 number:5, p 761
sourceStr	In Children 10(2023), 5, p 761 volume:10 year:2023 number:5, p 761
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topic_facet	COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning Pediatrics
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container_title	Children
authorswithroles_txt_mv	Dejan Dobrijević @@aut@@ Jelena Antić @@aut@@ Goran Rakić @@aut@@ Jasmina Katanić @@aut@@ Ljiljana Andrijević @@aut@@ Kristian Pastor @@aut@@
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callnumber-first	R - Medicine
author	Dejan Dobrijević
spellingShingle	Dejan Dobrijević misc RJ1-570 misc COVID-19 misc SARS-CoV-2 misc influenza misc diagnosis misc laboratory parameters misc machine learning misc Pediatrics Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach
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topic_title	RJ1-570 Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach COVID-19 SARS-CoV-2 influenza diagnosis laboratory parameters machine learning
topic	misc RJ1-570 misc COVID-19 misc SARS-CoV-2 misc influenza misc diagnosis misc laboratory parameters misc machine learning misc Pediatrics
topic_unstemmed	misc RJ1-570 misc COVID-19 misc SARS-CoV-2 misc influenza misc diagnosis misc laboratory parameters misc machine learning misc Pediatrics
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title	Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach
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title_full	Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach
author_sort	Dejan Dobrijević
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author_browse	Dejan Dobrijević Jelena Antić Goran Rakić Jasmina Katanić Ljiljana Andrijević Kristian Pastor
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author-letter	Dejan Dobrijević
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title_sort	clinical hematochemical parameters in differential diagnosis between pediatric sars-cov-2 and influenza virus infection: an automated machine learning approach
callnumber	RJ1-570
title_auth	Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach
abstract	Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.
abstractGer	Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.
abstract_unstemmed	Background: The influenza virus and the novel beta coronavirus (SARS-CoV-2) have similar transmission characteristics, and it is very difficult to distinguish them clinically. With the development of information technologies, novel opportunities have arisen for the application of intelligent software systems in disease diagnosis and patient triage. Methods: A cross-sectional study was conducted on 268 infants: 133 infants with a SARS-CoV-2 infection and 135 infants with an influenza virus infection. In total, 10 hematochemical variables were used to construct an automated machine learning model. Results: An accuracy range from 53.8% to 60.7% was obtained by applying support vector machine, random forest, k-nearest neighbors, logistic regression, and neural network models. Alternatively, an automated model convincingly outperformed other models with an accuracy of 98.4%. The proposed automated algorithm recommended a random tree model, a randomization-based ensemble method, as the most appropriate for the given dataset. Conclusions: The application of automated machine learning in clinical practice can contribute to more objective, accurate, and rapid diagnosis of SARS-CoV-2 and influenza virus infections in children.
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title_short	Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach
url	https://doi.org/10.3390/children10050761 https://doaj.org/article/5676640497a54e259a49b014a4991dd7 https://www.mdpi.com/2227-9067/10/5/761 https://doaj.org/toc/2227-9067
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author2	Jelena Antić Goran Rakić Jasmina Katanić Ljiljana Andrijević Kristian Pastor
author2Str	Jelena Antić Goran Rakić Jasmina Katanić Ljiljana Andrijević Kristian Pastor
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up_date	2024-07-03T22:57:35.387Z
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