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Cited by 179COVID-19 Clinical Research StudiesCOVID-19 diagnosis using AISARS-CoV-2 detection and testing

The DOI matches its Crossref record; 41 of the 43 references it cites were found in a registry or an index.

COVID-19 diagnosis by routine blood tests using machine learning

Open access
Kukar, Matjaž, Gunčar, Gregor, Vovko, Tomaž, Podnar, Simon, Černelč, Peter, Brvar, Miran, Zalaznik, Mateja, Notar, Mateja, Moškon, Sašo, Notar, Marko·2021·Scientific Reports
DOI 10.1038/s41598-021-90265-9PMC PMC8144373
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Abstract

Physicians taking care of patients with COVID-19 have described different changes in routine blood parameters. However, these changes hinder them from performing COVID-19 diagnoses. We constructed a machine learning model for COVID-19 diagnosis that was based and cross-validated on the routine blood tests of 5333 patients with various bacterial and viral infections, and 160 COVID-19-positive patients. We selected the operational ROC point at a sensitivity of 81.9% and a specificity of 97.9%. The cross-validated AUC was 0.97. The five most useful routine blood parameters for COVID-19 diagnosis according to the feature importance scoring of the XGBoost algorithm were: MCHC, eosinophil count, albumin, INR, and prothrombin activity percentage. t-SNE visualization showed that the blood parameters of the patients with a severe COVID-19 course are more like the parameters of a bacterial than a viral infection. The reported diagnostic accuracy is at least comparable and probably complementary to RT-PCR and chest CT studies. Patients with fever, cough, myalgia, and other symptoms can now have initial routine blood tests assessed by our diagnostic tool. All patients with a positive COVID-19 prediction would then undergo standard RT-PCR studies to confirm the diagnosis. We believe that our results represent a significant contribution to improvements in COVID-19 diagnosis.

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Figure 1 A flow chart of patients included in the model building and validation process.
Figure 1 A flow chart of patients included in the model building and validation process.CC BY · PubMed Central
Figure 2 Blood parameters sorted by their XGBoost importance score. More important parameters are shown on the left. Group median values and IQR of the blood parameters used in model building are shown, centered, and scaled to reference intervals. Median bar for the C-reactive protein in bacterial infections is out of the scale at 38 mg/L. Groups (COVID-19/other virus/bacteria) were evaluated by the Anderson–Darling test. The significance levels (0.05 or 0.01) of the test results are depicted at the bottom of the figure.
Figure 2 Blood parameters sorted by their XGBoost importance score. More important parameters are shown on the left. Group median values and IQR of the blood parameters used in model building are shown, centered, and scaled to reference intervals. Median bar for the C-reactive protein in bacterial infections is out of the scale at 38 mg/L. Groups (COVID-19/other virus/bacteria) were evaluated by the Anderson–Darling test. The significance levels (0.05 or 0.01) of the test results are depicted at the bottom of the figure.CC BY · PubMed Central
Figure 3 Visualization of bacteria/virus/COVID-19 parameter space with t-SNE method. Each dot represents a patient or more specifically, an embedding of his/her blood parameters into a two-dimensional space, and its color represents the group. Blue dots represent patients with viral infections other than COVID-19, orange dots patients with bacterial infections and red dots patients with COVID-19. Green dots in panel (a) represent COVID-19 patients who died (10 patients) and in panel (b) COVID-19 patients diagnosed with acute respiratory failure (38 patients). Medoids of bacteria/virus/COVID-19/”COVID-19 death” groups on panel (a) and bacteria/virus/COVID-19/”COVID-19 ARF” groups on panel (b) are also marked.
Figure 3 Visualization of bacteria/virus/COVID-19 parameter space with t-SNE method. Each dot represents a patient or more specifically, an embedding of his/her blood parameters into a two-dimensional space, and its color represents the group. Blue dots represent patients with viral infections other than COVID-19, orange dots patients with bacterial infections and red dots patients with COVID-19. Green dots in panel (a) represent COVID-19 patients who died (10 patients) and in panel (b) COVID-19 patients diagnosed with acute respiratory failure (38 patients). Medoids of bacteria/virus/COVID-19/”COVID-19 death” groups on panel (a) and bacteria/virus/COVID-19/”COVID-19 ARF” groups on panel (b) are also marked.CC BY · PubMed Central
Figure 4 ROC, PR (precision-recall), and F2 curves for COVID-19 diagnosis calculated from the training data using ten-fold stratified cross-validation. Vertical and horizontal dashed lines connect the F2 (gray) max point with the PR curve (orange) and the ROC curve (blue) in order to obtain the operational ROC point with sensitivity = 0.819, specificity = 0.979 (depicted with red dots), and AUC = 0.97.
Figure 4 ROC, PR (precision-recall), and F2 curves for COVID-19 diagnosis calculated from the training data using ten-fold stratified cross-validation. Vertical and horizontal dashed lines connect the F2 (gray) max point with the PR curve (orange) and the ROC curve (blue) in order to obtain the operational ROC point with sensitivity = 0.819, specificity = 0.979 (depicted with red dots), and AUC = 0.97.CC BY · PubMed Central

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Cites

Research works printed but not linked: 2

  1. Nielsen, D. Tree boosting with XGBoost – why does XGBoost win “every” machine learning competition? [Master’s thesis] (Norwegian University of Science and Technology, 2016).
  2. Flach, P., Hernández-Orallo, J. & Ferri, C. A coherent interpretation of AUC as a measure of aggregated classification performance. In The 28th International Conference on Machine Learning, ICML’11 (eds Getoor, L. & Scheffer, T.) 657–664 (Omnipress, 2011).

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Metadata

Authors
Kukar, M., Gunčar, G., Vovko, T., Podnar, S., Černelč, P., Brvar, M., Zalaznik, M., Notar, M., Moškon, S., & Notar, M.
Affiliations
  • University of Ljubljana (SI)
  • Ljubljana University Medical Centre (SI)
Corresponding author
  • Notar, Mateja
  • Notar, Marko
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2021-05-24
Volume
11
Issue
1
Pages
10738-10738
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-021-90265-9
PMC
PMC8144373
OpenAlex
W3033612360
Cited by
179
References
43
Citations by year
2026: 7 · 2025: 18 · 2024: 25 · 2023: 47 · 2022: 50 · 2021: 27
FWCI
11.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
Smart Blood Analytics Swiss SA
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
Keywords
COVID-19 diagnosis, routine blood tests, machine learning, XGBoost, feature importance, mean corpuscular hemoglobin concentration, eosinophil count, albumin, international normalized ratio, prothrombin activity, t-SNE visualization, diagnostic accuracy
MeSH terms
Machine Learning, COVID-19, SARS-CoV-2, Aged, Eosinophils, Female, Hematologic Tests, Humans, Male, Prothrombin, ROC Curve, Sensitivity and Specificity, Serum Albumin, Severity of Illness Index, Thorax, Tomography, X-Ray Computed, Biomarkers, Area Under Curve

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