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...
| Publicado en: | Children Vol. 10; no. 5; pp. 761 - 768 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
MDPI
May2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163938733&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163938733 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22279067 LW6K jtl: Children issn: 22279067 maglogo: N pubinfo: dt: May2023 vid: 10 iid: 5 pid: 97109 pub: MDPI artinfo: ui: 163938733 163938733 163938733 10.3390/children10050761 163938733 ppf: 761 ppct: 7 formats: tig: atl: Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach. aug: au: Dobrijević, Dejan Antić, Jelena Rakić, Goran Katanić, Jasmina Andrijević, Ljiljana Pastor, Kristian affil: Faculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia sug: subj: COVID-19 Diagnosis Influenza, Human Diagnosis Machine Learning Methods Diagnosis, Differential Pediatric Care Human Male Female Infant Serbia Cross Sectional Studies Descriptive Statistics Inferential Statistics Data Analysis Software Unpaired T-Tests Mann-Whitney U Test Spearman's Rank Correlation Coefficient Support Vector Machine Random Forest Algorithms Predictive Value of Tests Infant: 1-23 months Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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