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10.1038/s41551-020-00633-5 http://scihub22266oqcxt.onion/10.1038/s41551-020-00633-5 33208927!7723858!33208927     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 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 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Biomed+Eng 2020 ; 4 (12): 1197-1207 Nephropedia Template TP {{tp|p=33208927|t=2020. Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning |pdf=|usr=}}{{33208927}} gab.com Text Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning JO: Nat Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=33208927 #FOAvip Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19. Twit Text FOAVip Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning ????Nat Biomed Eng http://www.kidney.de/pget.php?&tw=1&pmid=33208927 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33208927 #FOAvip English Wikipedia <ref>{{Cite pmid|33208927}}</ref> Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning #MMPMID33208927 Ning W; Lei S; Yang J; Cao Y; Jiang P; Yang Q; Zhang J; Wang X; Chen F; Geng Z; Xiong L; Zhou H; Guo Y; Zeng Y; Shi H; Wang L; Xue Y; Wang Z Nat Biomed Eng 2020[Dec]; 4 (12): 1197-1207 PMID33208927show ga Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19. |Algorithms[MESH] |COVID-19/*pathology/*virology[MESH] |Deep Learning[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Pandemics[MESH] |Pneumonia, Viral/*pathology/*virology[MESH] |ROC Curve[MESH] |SARS-CoV-2/pathogenicity[MESH]Open resource of clinical data from patients with pneumonia for the pr(epmc).pdf DeepDyve Pubget Overpricing