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10.1186/s12911-020-01266-z http://scihub22266oqcxt.onion/10.1186/s12911-020-01266-z 32993652!7522928!32993652     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Inform+Decis+Mak 2020 ; 20 (1): 247 Nephropedia Template TP {{tp|p=32993652|t=2020. Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis |pdf=|usr=}}{{32993652}} gab.com Text Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=32993652 #FOAvip BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups. Twit Text FOAVip Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=32993652 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32993652 #FOAvip English Wikipedia <ref>{{Cite pmid|32993652}}</ref> Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis #MMPMID32993652 Li WT; Ma J; Shende N; Castaneda G; Chakladar J; Tsai JC; Apostol L; Honda CO; Xu J; Wong LM; Zhang T; Lee A; Gnanasekar A; Honda TK; Kuo SZ; Yu MA; Chang EY; Rajasekaran MR; Ongkeko WM BMC Med Inform Decis Mak 2020[Sep]; 20 (1): 247 PMID32993652show ga BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups. |*Machine Learning[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*methods[MESH] |Computer Simulation[MESH] |Coronavirus Infections/classification/*diagnosis[MESH] |Datasets as Topic[MESH] |Diagnosis, Differential[MESH] |Female[MESH] |Humans[MESH] |Influenza A virus[MESH] |Influenza, Human/*diagnosis[MESH] |Male[MESH] |Pandemics/classification[MESH] |Pneumonia, Viral/classification/*diagnosis[MESH] |SARS-CoV-2[MESH]Using machine learning of clinical data to diagnose COVID-19: a system(epmc).pdf DeepDyve Pubget Overpricing