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10.3390/jcm10040570 http://scihub22266oqcxt.onion/10.3390/jcm10040570 33546319!7913595!33546319     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Clin+Med 2021 ; 10 (4): ä Nephropedia Template TP {{tp|p=33546319|t=2021. Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach |pdf=|usr=}}{{33546319}} gab.com Text Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach JO: J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=33546319 #FOAvip The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction. Twit Text FOAVip Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach ????J Clin Med http://www.kidney.de/pget.php?&tw=1&pmid=33546319 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33546319 #FOAvip English Wikipedia <ref>{{Cite pmid|33546319}}</ref> Loss of Smell and Taste Can Accurately Predict COVID-19 Infection: A Machine-Learning Approach #MMPMID33546319 Callejon-Leblic MA; Moreno-Luna R; Del Cuvillo A; Reyes-Tejero IM; Garcia-Villaran MA; Santos-Pena M; Maza-Solano JM; Martin-Jimenez DI; Palacios-Garcia JM; Fernandez-Velez C; Gonzalez-Garcia J; Sanchez-Calvo JM; Solanellas-Soler J; Sanchez-Gomez S J Clin Med 2021[Feb]; 10 (4): ä PMID33546319show ga The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction. äLoss of Smell and Taste Can Accurately Predict COVID-19 Infection: A (epmc).pdf DeepDyve Pubget Overpricing