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10.1007/s11739-020-02475-0 http://scihub22266oqcxt.onion/10.1007/s11739-020-02475-0 32812204!7433773!32812204     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Intern+Emerg+Med 2020 ; 15 (8): 1435-1443 Nephropedia Template TP {{tp|p=32812204|t=2020. Utilization of machine-learning models to accurately predict the risk for critical COVID-19 |pdf=|usr=}}{{32812204}} gab.com Text Utilization of machine-learning models to accurately predict the risk for critical COVID-19 JO: Intern Emerg Med http://www.kidney.de/pget.php?&tw=1&pmid=32812204 #FOAvip Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic. Twit Text FOAVip Utilization of machine-learning models to accurately predict the risk for critical COVID-19 ????Intern Emerg Med http://www.kidney.de/pget.php?&tw=1&pmid=32812204 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32812204 #FOAvip English Wikipedia <ref>{{Cite pmid|32812204}}</ref> Utilization of machine-learning models to accurately predict the risk for critical COVID-19 #MMPMID32812204 Assaf D; Gutman Y; Neuman Y; Segal G; Amit S; Gefen-Halevi S; Shilo N; Epstein A; Mor-Cohen R; Biber A; Rahav G; Levy I; Tirosh A Intern Emerg Med 2020[Nov]; 15 (8): 1435-1443 PMID32812204show ga Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic. |APACHE[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19[MESH] |Coronavirus Infections/*complications/diagnosis/epidemiology[MESH] |Critical Illness/mortality/therapy[MESH] |Female[MESH] |Hospitalization/statistics & numerical data[MESH] |Humans[MESH] |Machine Learning/*trends[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*complications/diagnosis/epidemiology[MESH] |ROC Curve[MESH] |Retrospective Studies[MESH]Utilization of machine-learning models to accurately predict the risk (epmc).pdf DeepDyve Pubget Overpricing