Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study
Coronavirus 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 b...
| Autores: | , , , , , , , , , , |
|---|---|
| Tipo de documento: | artigo |
| Data de publicação: | 2021 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/367227 |
| Acesso em linha: | https://hdl.handle.net/2117/367227 https://dx.doi.org/10.3389/fmed.2021.768467 |
| Access Level: | Acceso aberto |
| Palavra-chave: | COVID-19 (Disease) Pandèmia de COVID-19, 2020- Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Validation studies Machine learning COVID-19 Computer-aided diagnosis Screening |
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Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort studyMarateb, Hamid Reza|||0000-0003-4408-2397Nezhad, Farzad ZiaieMohebian, Mohammad RezaSamí, RaminJavanmard, Shaghayegh HaghjooyDehghan Niri, FatemehAkafzadeh Savari, MahsaMansourian Gharakozlou, MarjanMañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083Wolkewitz, MartinBinder, HaraldCOVID-19 (Disease)Pandèmia de COVID-19, 2020-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::BioinformàticaValidation studiesMachine learningCOVID-19Computer-aided diagnosisScreeningCoronavirus 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.Peer ReviewedFrontiers Media SA20212021-11-1820222022-05-11journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/367227https://dx.doi.org/10.3389/fmed.2021.768467reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3672272026-05-27T15:37:01Z |
| dc.title.none.fl_str_mv |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| title |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| spellingShingle |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study Marateb, Hamid Reza|||0000-0003-4408-2397 COVID-19 (Disease) Pandèmia de COVID-19, 2020- Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Validation studies Machine learning COVID-19 Computer-aided diagnosis Screening |
| title_short |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| title_full |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| title_fullStr |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| title_full_unstemmed |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| title_sort |
Automatic classification between COVID-19 and Non-COVID-19 pneumonia using symptoms, comorbidities, and laboratory findings : the Khorshid COVID cohort study |
| dc.creator.none.fl_str_mv |
Marateb, Hamid Reza|||0000-0003-4408-2397 Nezhad, Farzad Ziaie Mohebian, Mohammad Reza Samí, Ramin Javanmard, Shaghayegh Haghjooy Dehghan Niri, Fatemeh Akafzadeh Savari, Mahsa Mansourian Gharakozlou, Marjan Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083 Wolkewitz, Martin Binder, Harald |
| author |
Marateb, Hamid Reza|||0000-0003-4408-2397 |
| author_facet |
Marateb, Hamid Reza|||0000-0003-4408-2397 Nezhad, Farzad Ziaie Mohebian, Mohammad Reza Samí, Ramin Javanmard, Shaghayegh Haghjooy Dehghan Niri, Fatemeh Akafzadeh Savari, Mahsa Mansourian Gharakozlou, Marjan Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083 Wolkewitz, Martin Binder, Harald |
| author_role |
author |
| author2 |
Nezhad, Farzad Ziaie Mohebian, Mohammad Reza Samí, Ramin Javanmard, Shaghayegh Haghjooy Dehghan Niri, Fatemeh Akafzadeh Savari, Mahsa Mansourian Gharakozlou, Marjan Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083 Wolkewitz, Martin Binder, Harald |
| author2_role |
author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
COVID-19 (Disease) Pandèmia de COVID-19, 2020- Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Validation studies Machine learning COVID-19 Computer-aided diagnosis Screening |
| topic |
COVID-19 (Disease) Pandèmia de COVID-19, 2020- Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica Validation studies Machine learning COVID-19 Computer-aided diagnosis Screening |
| description |
Coronavirus 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. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 2021-11-18 2022 2022-05-11 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/367227 https://dx.doi.org/10.3389/fmed.2021.768467 |
| url |
https://hdl.handle.net/2117/367227 https://dx.doi.org/10.3389/fmed.2021.768467 |
| dc.language.none.fl_str_mv |
Inglés eng |
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Inglés |
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eng |
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open access http://purl.org/coar/access_right/c_abf2 Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ |
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openAccess |
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application/pdf |
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Frontiers Media SA |
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Frontiers Media SA |
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reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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