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10.1093/jamia/ocab186 http://scihub22266oqcxt.onion/10.1093/jamia/ocab186 34415311!8714262!34415311     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34415311&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       J+Am+Med+Inform+Assoc 2021 ; 29 (1): 12-21 Nephropedia Template TP {{tp|p=34415311|t=2021. Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing |pdf=|usr=}}{{34415311}} gab.com Text Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=34415311 #FOAvip OBJECTIVE: The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing. MATERIALS AND METHODS: To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a Web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information. RESULTS: The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81% to 92% and to enable pooled testing with a negative predictive value of 90% to 91%, reducing the required tests to about 63%. CONCLUSIONS: SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing. Twit Text FOAVip Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=34415311 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34415311 #FOAvip English Wikipedia <ref>{{Cite pmid|34415311}}</ref> Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing #MMPMID34415311 Meystre SM; Heider PM; Kim Y; Davis M; Obeid J; Madory J; Alekseyenko AV J Am Med Inform Assoc 2021[Dec]; 29 (1): 12-21 PMID34415311show ga OBJECTIVE: The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing. MATERIALS AND METHODS: To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a Web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information. RESULTS: The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81% to 92% and to enable pooled testing with a negative predictive value of 90% to 91%, reducing the required tests to about 63%. CONCLUSIONS: SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing. |*COVID-19[MESH] |COVID-19 Testing[MESH] |Humans[MESH] |Natural Language Processing[MESH] |Pandemics[MESH]Natural language processing enabling COVID-19 predictive analytics to (epmc).pdf DeepDyve Pubget Overpricing