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10.1515/jib-2020-0050 http://scihub22266oqcxt.onion/10.1515/jib-2020-0050 33675198!8035960!33675198     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Integr+Bioinform 2021 ; 18 (1): 3-8 Nephropedia Template TP {{tp|p=33675198|t=2021. Clinical presentation of COVID-19 - a model derived by a machine learning algorithm |pdf=|usr=}}{{33675198}} gab.com Text Clinical presentation of COVID-19 - a model derived by a machine learning algorithm JO: J Integr Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33675198 #FOAvip COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up. Twit Text FOAVip Clinical presentation of COVID-19 - a model derived by a machine learning algorithm ????J Integr Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=33675198 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33675198 #FOAvip English Wikipedia <ref>{{Cite pmid|33675198}}</ref> Clinical presentation of COVID-19 - a model derived by a machine learning algorithm #MMPMID33675198 Yousef M; Showe LC; Ben Shlomo I J Integr Bioinform 2021[Mar]; 18 (1): 3-8 PMID33675198show ga COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up. |*COVID-19[MESH] |*Machine Learning[MESH] |Algorithms[MESH] |Humans[MESH] |Male[MESH] |Pandemics[MESH]Clinical presentation of COVID-19 - a model derived by a machine learn(epmc).pdf DeepDyve Pubget Overpricing