Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1121/10.0003434 http://scihub22266oqcxt.onion/10.1121/10.0003434 33639822!7928231!33639822     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Acoust+Soc+Am 2021 ; 149 (2): 1120 Nephropedia Template TP {{tp|p=33639822|t=2021. Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires |pdf=|usr=}}{{33639822}} gab.com Text Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires JO: J Acoust Soc Am http://www.kidney.de/pget.php?&tw=1&pmid=33639822 #FOAvip The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people in the past few months. In this context, advanced artificial intelligence techniques are brought to the forefront as a response to the ongoing fight toward reducing the impact of this global health crisis. In this study, potential use-cases of intelligent speech analysis for COVID-19 identification are being developed. By analyzing speech recordings from COVID-19 positive and negative patients, we constructed audio- and symptomatic-based models to automatically categorize the health state of patients, whether they are COVID-19 positive or not. For this purpose, many acoustic features were established, and various machine learning algorithms are being utilized. Experiments show that an average accuracy of 80% was obtained estimating COVID-19 positive or negative, derived from multiple cough and vowel /a/ recordings, and an average accuracy of 83% was obtained estimating COVID-19 positive or negative patients by evaluating six symptomatic questions. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease. Twit Text FOAVip Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires ????J Acoust Soc Am http://www.kidney.de/pget.php?&tw=1&pmid=33639822 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33639822 #FOAvip English Wikipedia <ref>{{Cite pmid|33639822}}</ref> Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires #MMPMID33639822 Shimon C; Shafat G; Dangoor I; Ben-Shitrit A J Acoust Soc Am 2021[Feb]; 149 (2): 1120 PMID33639822show ga The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people in the past few months. In this context, advanced artificial intelligence techniques are brought to the forefront as a response to the ongoing fight toward reducing the impact of this global health crisis. In this study, potential use-cases of intelligent speech analysis for COVID-19 identification are being developed. By analyzing speech recordings from COVID-19 positive and negative patients, we constructed audio- and symptomatic-based models to automatically categorize the health state of patients, whether they are COVID-19 positive or not. For this purpose, many acoustic features were established, and various machine learning algorithms are being utilized. Experiments show that an average accuracy of 80% was obtained estimating COVID-19 positive or negative, derived from multiple cough and vowel /a/ recordings, and an average accuracy of 83% was obtained estimating COVID-19 positive or negative patients by evaluating six symptomatic questions. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease. |*Artificial Intelligence/trends[MESH] |*Cues[MESH] |*Surveys and Questionnaires[MESH] |COVID-19/*diagnosis/physiopathology/psychology[MESH] |Cough/*diagnosis/physiopathology/psychology[MESH] |Humans[MESH]Artificial intelligence enabled preliminary diagnosis for COVID-19 fro(epmc).pdf DeepDyve Pubget Overpricing