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10.1017/S0950268820001727 http://scihub22266oqcxt.onion/10.1017/S0950268820001727 32746957!7426607!32746957     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Epidemiol+Infect 2020 ; 148 (ä): e168 Nephropedia Template TP {{tp|p=32746957|t=2020. Development and validation of prognosis model of mortality risk in patients with COVID-19 |pdf=|usr=}}{{32746957}} gab.com Text Development and validation of prognosis model of mortality risk in patients with COVID-19 JO: Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32746957 #FOAvip This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission. Twit Text FOAVip Development and validation of prognosis model of mortality risk in patients with COVID-19 ????Epidemiol Infect http://www.kidney.de/pget.php?&tw=1&pmid=32746957 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32746957 #FOAvip English Wikipedia <ref>{{Cite pmid|32746957}}</ref> Development and validation of prognosis model of mortality risk in patients with COVID-19 #MMPMID32746957 Ma X; Ng M; Xu S; Xu Z; Qiu H; Liu Y; Lyu J; You J; Zhao P; Wang S; Tang Y; Cui H; Yu C; Wang F; Shao F; Sun P; Tang Z Epidemiol Infect 2020[Aug]; 148 (ä): e168 PMID32746957show ga This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission. |*Logistic Models[MESH] |*Machine Learning[MESH] |Adolescent[MESH] |Adult[MESH] |Aged[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*mortality/*therapy[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*mortality/*therapy[MESH] |Prognosis[MESH] |ROC Curve[MESH] |Reproducibility of Results[MESH] |Retrospective Studies[MESH] |Risk Assessment/methods[MESH]Development and validation of prognosis model of mortality risk in pat(epmc).pdf DeepDyve Pubget Overpricing