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10.1038/s41598-021-90265-9 http://scihub22266oqcxt.onion/10.1038/s41598-021-90265-9 34031483!8144373!34031483     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 10738 Nephropedia Template TP {{tp|p=34031483|t=2021. COVID-19 diagnosis by routine blood tests using machine learning |pdf=|usr=}}{{34031483}} gab.com Text COVID-19 diagnosis by routine blood tests using machine learning JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34031483 #FOAvip 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. Twit Text FOAVip COVID-19 diagnosis by routine blood tests using machine learning ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34031483 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34031483 #FOAvip English Wikipedia <ref>{{Cite pmid|34031483}}</ref> COVID-19 diagnosis by routine blood tests using machine learning #MMPMID34031483 Kukar M; Guncar G; Vovko T; Podnar S; Cernelc P; Brvar M; Zalaznik M; Notar M; Moskon S; Notar M Sci Rep 2021[May]; 11 (1): 10738 PMID34031483show ga 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. |*Machine Learning[MESH] |Aged[MESH] |Area Under Curve[MESH] |Biomarkers/blood[MESH] |COVID-19/*diagnosis/pathology/virology[MESH] |Eosinophils/cytology[MESH] |Female[MESH] |Hematologic Tests[MESH] |Humans[MESH] |Male[MESH] |Prothrombin/metabolism[MESH] |ROC Curve[MESH] |SARS-CoV-2/isolation & purification[MESH] |Sensitivity and Specificity[MESH] |Serum Albumin/analysis[MESH] |Severity of Illness Index[MESH] |Thorax/diagnostic imaging[MESH]COVID-19 diagnosis by routine blood tests using machine learning (epmc).pdf DeepDyve Pubget Overpricing