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10.1371/journal.pone.0255301 http://scihub22266oqcxt.onion/10.1371/journal.pone.0255301 34329354!8323880!34329354     free     free     free Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 225.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (7): e0255301 Nephropedia Template TP {{tp|p=34329354|t=2021. Deep learning with robustness to missing data: A novel approach to the detection of COVID-19 |pdf=|usr=}}{{34329354}} gab.com Text Deep learning with robustness to missing data: A novel approach to the detection of COVID-19 JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34329354 #FOAvip In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around the world differ enormously in what laboratory tests or chest imaging may be available, DFCN is designed to be robust to missing input data. An ablation study extensively evaluates the performance benefits of the DFCN as well as its robustness to missing inputs. Data from 1088 patients with confirmed RT-PCR results are obtained from two independent medical facilities. The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model. Training and test datasets are taken from different medical facilities. Data is made publicly available. The performance of DFCN in predicting the RT-PCR result is compared with 3 related architectures as well as a Random Forest baseline. All models are trained with varying levels of masked input data to encourage robustness to missing inputs. Missing data is simulated at test time by masking inputs randomly. DFCN outperforms all other models with statistical significance using random subsets of input data with 2-27 available inputs. When all 28 inputs are available DFCN obtains an AUC of 0.924, higher than any other model. Furthermore, with clinically meaningful subsets of parameters consisting of just 6 and 7 inputs respectively, DFCN achieves higher AUCs than any other model, with values of 0.909 and 0.919. Twit Text FOAVip Deep learning with robustness to missing data: A novel approach to the detection of COVID-19 ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34329354 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34329354 #FOAvip English Wikipedia <ref>{{Cite pmid|34329354}}</ref> Deep learning with robustness to missing data: A novel approach to the detection of COVID-19 #MMPMID34329354 Calli E; Murphy K; Kurstjens S; Samson T; Herpers R; Smits H; Rutten M; van Ginneken B PLoS One 2021[]; 16 (7): e0255301 PMID34329354show ga In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around the world differ enormously in what laboratory tests or chest imaging may be available, DFCN is designed to be robust to missing input data. An ablation study extensively evaluates the performance benefits of the DFCN as well as its robustness to missing inputs. Data from 1088 patients with confirmed RT-PCR results are obtained from two independent medical facilities. The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model. Training and test datasets are taken from different medical facilities. Data is made publicly available. The performance of DFCN in predicting the RT-PCR result is compared with 3 related architectures as well as a Random Forest baseline. All models are trained with varying levels of masked input data to encourage robustness to missing inputs. Missing data is simulated at test time by masking inputs randomly. DFCN outperforms all other models with statistical significance using random subsets of input data with 2-27 available inputs. When all 28 inputs are available DFCN obtains an AUC of 0.924, higher than any other model. Furthermore, with clinically meaningful subsets of parameters consisting of just 6 and 7 inputs respectively, DFCN achieves higher AUCs than any other model, with values of 0.909 and 0.919. |*COVID-19 Nucleic Acid Testing[MESH] |*Databases, Factual[MESH] |*Deep Learning[MESH] |*Models, Theoretical[MESH] |*SARS-CoV-2[MESH] |COVID-19/*diagnosis[MESH] |Humans[MESH]Deep learning with robustness to missing data: A novel approach to the(epmc).pdf DeepDyve Pubget Overpricing