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10.1038/s41467-020-17280-8 http://scihub22266oqcxt.onion/10.1038/s41467-020-17280-8 32669540!7363899!32669540     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32669540&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32669540.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Nat+Commun 2020 ; 11 (1): 3543 Nephropedia Template TP {{tp|p=32669540|t=2020. Early triage of critically ill COVID-19 patients using deep learning |pdf=|usr=}}{{32669540}} gab.com Text Early triage of critically ill COVID-19 patients using deep learning JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32669540 #FOAvip The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources. Twit Text FOAVip Early triage of critically ill COVID-19 patients using deep learning ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=32669540 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32669540 #FOAvip English Wikipedia <ref>{{Cite pmid|32669540}}</ref> Early triage of critically ill COVID-19 patients using deep learning #MMPMID32669540 Liang W; Yao J; Chen A; Lv Q; Zanin M; Liu J; Wong S; Li Y; Lu J; Liang H; Chen G; Guo H; Guo J; Zhou R; Ou L; Zhou N; Chen H; Yang F; Han X; Huan W; Tang W; Guan W; Chen Z; Zhao Y; Sang L; Xu Y; Wang W; Li S; Lu L; Zhang N; Zhong N; Huang J; He J Nat Commun 2020[Jul]; 11 (1): 3543 PMID32669540show ga The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources. |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnosis/*pathology[MESH] |Critical Illness[MESH] |Deep Learning/*statistics & numerical data[MESH] |Hospitalization[MESH] |Humans[MESH] |Middle Aged[MESH] |Models, Theoretical[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis/*pathology[MESH] |Prognosis[MESH] |Risk[MESH] |SARS-CoV-2[MESH] |Survival Analysis[MESH]Early triage of critically ill COVID-19 patients using deep learning (epmc).pdf DeepDyve Pubget Overpricing