Your browser doesn't support javascript.
Automatic Classification Between COVID-19 and Non-COVID-19 Pneumonia Using Symptoms, Comorbidities, and Laboratory Findings: The Khorshid COVID Cohort Study.
Marateb, Hamid Reza; Ziaie Nezhad, Farzad; Mohebian, Mohammad Reza; Sami, Ramin; Haghjooy Javanmard, Shaghayegh; Dehghan Niri, Fatemeh; Akafzadeh-Savari, Mahsa; Mansourian, Marjan; Mañanas, Miquel Angel; Wolkewitz, Martin; Binder, Harald.
  • Marateb HR; The Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran.
  • Ziaie Nezhad F; The Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran.
  • Mohebian MR; Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada.
  • Sami R; Department of Internal Medicine, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Haghjooy Javanmard S; Department of Physiology, Applied Physiology Research Center, School of Medicine, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Dehghan Niri F; School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Akafzadeh-Savari M; Isfahan Clinical Toxicology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Mansourian M; Automatic Control Department (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain.
  • Mañanas MA; Department of Epidemiology and Biostatistics, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Wolkewitz M; Automatic Control Department (ESAII), Biomedical Engineering Research Centre (CREB), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona, Spain.
  • Binder H; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Spain.
Front Med (Lausanne) ; 8: 768467, 2021.
Article in English | MEDLINE | ID: covidwho-1555763
ABSTRACT
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: Front Med (Lausanne) Year: 2021 Document Type: Article Affiliation country: Fmed.2021.768467

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Diagnostic study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: Front Med (Lausanne) Year: 2021 Document Type: Article Affiliation country: Fmed.2021.768467