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10.2147/RMHP.S291498 http://scihub22266oqcxt.onion/10.2147/RMHP.S291498 33623450!7895911!33623450     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Risk+Manag+Healthc+Policy 2021 ; 14 (ä): 595-604 Nephropedia Template TP {{tp|p=33623450|t=2021. Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study |pdf=|usr=}}{{33623450}} gab.com Text Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study JO: Risk Manag Healthc Policy http://www.kidney.de/pget.php?&tw=1&pmid=33623450 #FOAvip BACKGROUND: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. METHODS: Laboratory-confirmed COVID-19 and influenza patients between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. RESULTS: Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 7:3, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI: 0.81, 0.87), respectively. CONCLUSION: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens. Twit Text FOAVip Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study ????Risk Manag Healthc Policy http://www.kidney.de/pget.php?&tw=1&pmid=33623450 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33623450 #FOAvip English Wikipedia <ref>{{Cite pmid|33623450}}</ref> Machine Learning-Based Decision Model to Distinguish Between COVID-19 and Influenza: A Retrospective, Two-Centered, Diagnostic Study #MMPMID33623450 Zhou X; Wang Z; Li S; Liu T; Wang X; Xia J; Zhao Y Risk Manag Healthc Policy 2021[]; 14 (ä): 595-604 PMID33623450show ga BACKGROUND: Considering the current situation of the novel coronavirus disease (COVID-19) epidemic control, it is highly likely that COVID-19 and influenza may coincide during the approaching winter season. However, there is no available tool that can rapidly and precisely distinguish between these two diseases in the absence of laboratory evidence of specific pathogens. METHODS: Laboratory-confirmed COVID-19 and influenza patients between December 1, 2019 and February 29, 2020, from Zhongnan Hospital of Wuhan University (ZHWU) and Wuhan No.1 Hospital (WNH) located in Wuhan, China, were included for analysis. A machine learning-based decision model was developed using the XGBoost algorithms. RESULTS: Data of 357 COVID-19 and 1893 influenza patients from ZHWU were split into a training and a testing set in the ratio 7:3, while the dataset from WNH (308 COVID-19 and 312 influenza patients) was preserved for an external test. Model-based decision tree selected age, serum high-sensitivity C-reactive protein and circulating monocytes as meaningful indicators for classifying COVID-19 and influenza cases. In the training, testing and external sets, the model achieved good performance in identifying COVID-19 from influenza cases with a corresponding area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI 0.93, 0.96), 0.93 (95% CI 0.90, 0.96), and 0.84 (95% CI: 0.81, 0.87), respectively. CONCLUSION: Machine learning provides a tool that can rapidly and accurately distinguish between COVID-19 and influenza cases. This finding would be particularly useful in regions with massive co-occurrences of COVID-19 and influenza cases while limited resources for laboratory testing of specific pathogens. äMachine Learning-Based Decision Model to Distinguish Between COVID-19 (epmc).pdf DeepDyve Pubget Overpricing