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10.1093/jamia/ocaa105 http://scihub22266oqcxt.onion/10.1093/jamia/ocaa105 32449766!7313981!32449766     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2020 ; 27 (8): 1321-1325 Nephropedia Template TP {{tp|p=32449766|t=2020. An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report |pdf=|usr=}}{{32449766}} gab.com Text An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=32449766 #FOAvip OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits. MATERIALS AND METHODS: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms. RESULTS: Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. CONCLUSIONS: Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response. Twit Text FOAVip An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=32449766 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32449766 #FOAvip English Wikipedia <ref>{{Cite pmid|32449766}}</ref> An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report #MMPMID32449766 Obeid JS; Davis M; Turner M; Meystre SM; Heider PM; O'Bryan EC; Lenert LA J Am Med Inform Assoc 2020[Aug]; 27 (8): 1321-1325 PMID32449766show ga OBJECTIVE: In an effort to improve the efficiency of computer algorithms applied to screening for coronavirus disease 2019 (COVID-19) testing, we used natural language processing and artificial intelligence-based methods with unstructured patient data collected through telehealth visits. MATERIALS AND METHODS: After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms. RESULTS: Text analytics revealed that concepts such as smell and taste were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an area under the receiver-operating characteristic curve of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling. CONCLUSIONS: Informatics tools such as natural language processing and artificial intelligence methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response. |*Artificial Intelligence[MESH] |*Natural Language Processing[MESH] |*Telemedicine[MESH] |Algorithms[MESH] |Betacoronavirus[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques[MESH] |Coronavirus Infections/*diagnosis[MESH] |Deep Learning[MESH] |Electronic Health Records[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |Organizational Case Studies[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |ROC Curve[MESH] |Risk Assessment[MESH] |SARS-CoV-2[MESH]An artificial intelligence approach to COVID-19 infection risk assessm(epmc).pdf DeepDyve Pubget Overpricing