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10.1038/s41746-020-00340-0 http://scihub22266oqcxt.onion/10.1038/s41746-020-00340-0 33083564!7538938!33083564     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       NPJ+Digit+Med 2020 ; 3 (ä): 129 Nephropedia Template TP {{tp|p=33083564|t=2020. Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements |pdf=|usr=}}{{33083564}} gab.com Text Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements JO: NPJ Digit Med http://www.kidney.de/pget.php?&tw=1&pmid=33083564 #FOAvip Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19. Twit Text FOAVip Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements ????NPJ Digit Med http://www.kidney.de/pget.php?&tw=1&pmid=33083564 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33083564 #FOAvip English Wikipedia <ref>{{Cite pmid|33083564}}</ref> Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements #MMPMID33083564 Sattler F; Ma J; Wagner P; Neumann D; Wenzel M; Schafer R; Samek W; Muller KR; Wiegand T NPJ Digit Med 2020[]; 3 (ä): 129 PMID33083564show ga Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19. äRisk estimation of SARS-CoV-2 transmission from bluetooth low energy (epmc).pdf DeepDyve Pubget Overpricing