{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T12:42:36Z","timestamp":1775047356391,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>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\u2013based methods with unstructured patient data collected through telehealth visits.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding\u2013based convolutional neural network for predicting COVID-19 test results based on patients\u2019 self-reported symptoms.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa105","type":"journal-article","created":{"date-parts":[[2020,5,22]],"date-time":"2020-05-22T03:22:50Z","timestamp":1590117770000},"page":"1321-1325","source":"Crossref","is-referenced-by-count":64,"title":["An artificial intelligence approach to COVID-19 infection risk assessment in virtual visits: A case report"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7193-7779","authenticated-orcid":false,"given":"Jihad S","family":"Obeid","sequence":"first","affiliation":[{"name":"Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA"},{"name":"Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA"}]},{"given":"Matthew","family":"Davis","sequence":"additional","affiliation":[{"name":"Information Solutions, Medical University of South Carolina, Charleston, South Carolina, USA"}]},{"given":"Matthew","family":"Turner","sequence":"additional","affiliation":[{"name":"Information Solutions, Medical University of South Carolina, Charleston, South Carolina, USA"}]},{"given":"Stephane M","family":"Meystre","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA"},{"name":"Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1589-4567","authenticated-orcid":false,"given":"Paul M","family":"Heider","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA"}]},{"given":"Edward C","family":"O'Bryan","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"}]},{"given":"Leslie A","family":"Lenert","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA"},{"name":"Department of Medicine, Medical University of South Carolina, Charleston, South Carolina, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,7,4]]},"reference":[{"issue":"8","key":"2020110613110387200_ocaa105-B1","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1001\/jama.2020.0757","article-title":"Coronavirus infections-more than just the common cold","volume":"323","author":"Paules","year":"2020","journal-title":"JAMA"},{"key":"2020110613110387200_ocaa105-B2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.5788","article-title":"The COVID-19 pandemic in the US: a clinical update","author":"Omer","year":"2020","journal-title":"JAMA"},{"issue":"15","key":"2020110613110387200_ocaa105-B3","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1001\/jama.2020.3882","article-title":"From containment to mitigation of COVID-19 in the US","volume":"323","author":"Parodi","year":"2020","journal-title":"JAMA"},{"issue":"11","key":"2020110613110387200_ocaa105-B4","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1001\/jama.2020.1585","article-title":"Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China","volume":"323","author":"Wang","year":"2020","journal-title":"JAMA"},{"key":"2020110613110387200_ocaa105-B5","doi-asserted-by":"crossref","DOI":"10.1001\/jamainternmed.2020.0994","article-title":"Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China","author":"Wu","year":"2020","journal-title":"JAMA Intern Med"},{"issue":"1","key":"2020110613110387200_ocaa105-B6","doi-asserted-by":"crossref","first-page":"206","DOI":"10.15265\/IY-2014-0006","article-title":"EHR Big Data Deep Phenotyping. 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