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  • COVID-19 diagnosis by routine blood tests using machine learning [Elektronski vir]
    Kukar, Matjaž ...
    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.
    Source: Scientific reports [Elektronski vir]. - ISSN 2045-2322 (Vol. 11, iss. 1, May 2021, str. 1-9)
    Type of material - e-article
    Publish date - 2021
    Language - english
    COBISS.SI-ID - 65807875

    Link(s):

    https://www.nature.com/articles/s41598-021-90265-9.pdf
    DOI

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