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10.1136/bmjhci-2020-100312 http://scihub22266oqcxt.onion/10.1136/bmjhci-2020-100312 34108143!8190987!34108143     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\34108143.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMJ+Health+Care+Inform 2021 ; 28 (1): ä Nephropedia Template TP {{tp|p=34108143|t=2021. Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs |pdf=|usr=}}{{34108143}} gab.com Text Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs JO: BMJ Health Care Inform http://www.kidney.de/pget.php?&tw=1&pmid=34108143 #FOAvip Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients. Twit Text FOAVip Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs ????BMJ Health Care Inform http://www.kidney.de/pget.php?&tw=1&pmid=34108143 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34108143 #FOAvip English Wikipedia <ref>{{Cite pmid|34108143}}</ref> Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs #MMPMID34108143 Makridis CA; Strebel T; Marconi V; Alterovitz G BMJ Health Care Inform 2021[Jun]; 28 (1): ä PMID34108143show ga Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients. |*Artificial Intelligence[MESH] |*Models, Statistical[MESH] |*Veterans[MESH] |COVID-19/*mortality[MESH] |Data Display[MESH] |Humans[MESH] |Risk Factors[MESH] |United States[MESH]Designing COVID-19 mortality predictions to advance clinical outcomes:(epmc).pdf DeepDyve Pubget Overpricing