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dc.contributor.authorMarateb, Hamid Reza
dc.contributor.authorNezhad, Farzad Ziaie
dc.contributor.authorMohebian, Mohammad Reza
dc.contributor.authorSamí, Ramin
dc.contributor.authorJavanmard, Shaghayegh Haghjooy
dc.contributor.authorDehghan Niri, Fatemeh
dc.contributor.authorAkafzadeh Savari, Mahsa
dc.contributor.authorMansourian Gharakozlou, Marjan
dc.contributor.authorMañanas Villanueva, Miguel Ángel
dc.contributor.authorWolkewitz, Martin
dc.contributor.authorBinder, Harald
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2022-05-11T10:16:10Z
dc.date.available2022-05-11T10:16:10Z
dc.date.issued2021-11-18
dc.identifier.citationMarateb, H.R. [et al.]. Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study. "Frontiers in Medicine", 18 Novembre 2021, vol. 8, núm. 768467, p. 768467:1-768467:14.
dc.identifier.issn2296-858X
dc.identifier.urihttp://hdl.handle.net/2117/367227
dc.description.abstractCoronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica
dc.subjectValidation studies
dc.subjectMachine learning
dc.subjectCOVID-19
dc.subjectComputer-aided diagnosis
dc.subjectScreening
dc.subject.lcshCOVID-19 (Disease)
dc.titleAutomatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study
dc.typeArticle
dc.subject.lemacPandèmia de COVID-19, 2020-
dc.contributor.groupUniversitat Politècnica de Catalunya. BIOART - BIOsignal Analysis for Rehabilitation and Therapy
dc.identifier.doi10.3389/fmed.2021.768467
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.frontiersin.org/articles/10.3389/fmed.2021.768467/full
dc.rights.accessOpen Access
local.identifier.drac32549347
dc.description.versionPostprint (published version)
local.citation.authorMarateb, H.R.; Nezhad, F.; Mohebian, M.; Samí, R.; Javanmard, S.; Dehghan, F.; Akafzadeh, M.; Mansourian, M.; Mañanas, M.A.; Wolkewitz, M.; Binder, H.
local.citation.publicationNameFrontiers in Medicine
local.citation.volume8
local.citation.number768467
local.citation.startingPage768467:1
local.citation.endingPage768467:14


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