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10.1080/07391102.2021.1966509 http://scihub22266oqcxt.onion/10.1080/07391102.2021.1966509 34463205!8425445!34463205     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34463205&cmd=llinks): Failed to open stream: HTTP request failed! 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Covid-19 rapid test by combining a Random Forest-based web system and blood tests |pdf=|usr=}}{{34463205}} gab.com Text Covid-19 rapid test by combining a Random Forest-based web system and blood tests JO: J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=34463205 #FOAvip The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%+/-0.851, kappa index of 0.858 +/- 0.017, sensitivity of 0.936 +/- 0.011, precision of 0.923 +/- 0.011, specificity of 0.921 +/- 0.012 and area under ROC of 0.984 +/- 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma. Twit Text FOAVip Covid-19 rapid test by combining a Random Forest-based web system and blood tests ????J Biomol Struct Dyn http://www.kidney.de/pget.php?&tw=1&pmid=34463205 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34463205 #FOAvip English Wikipedia <ref>{{Cite pmid|34463205}}</ref> Covid-19 rapid test by combining a Random Forest-based web system and blood tests #MMPMID34463205 Barbosa VAF; Gomes JC; de Santana MA; de Lima CL; Calado RB; Bertoldo Junior CR; Albuquerque JEA; de Souza RG; de Araujo RJE; Mattos Junior LAR; de Souza RE; Dos Santos WP J Biomol Struct Dyn 2022[]; 40 (22): 11948-11967 PMID34463205show ga The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%+/-0.851, kappa index of 0.858 +/- 0.017, sensitivity of 0.936 +/- 0.011, precision of 0.923 +/- 0.011, specificity of 0.921 +/- 0.012 and area under ROC of 0.984 +/- 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma. |*COVID-19/diagnosis[MESH] |Artificial Intelligence[MESH] |COVID-19 Testing[MESH] |Hematologic Tests[MESH] |Humans[MESH] |Random Forest[MESH]Covid-19 rapid test by combining a Random Forest-based web system and (epmc).pdf DeepDyve Pubget Overpricing