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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Patients with influenza and SARS-CoV2\/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital\/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A\/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ynaveena\/COVID-19-vs-Influenza\">https:\/\/github.com\/ynaveena\/COVID-19-vs-Influenza<\/jats:ext-link>\n                    and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.\n                  <\/jats:p>","DOI":"10.1038\/s41746-021-00467-8","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T06:04:47Z","timestamp":1622786687000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4454-4240","authenticated-orcid":false,"given":"Naveena","family":"Yanamala","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8036-2789","authenticated-orcid":false,"given":"Nanda H.","family":"Krishna","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8226-2319","authenticated-orcid":false,"given":"Quincy A.","family":"Hathaway","sequence":"additional","affiliation":[]},{"given":"Aditya","family":"Radhakrishnan","sequence":"additional","affiliation":[]},{"given":"Srinidhi","family":"Sunkara","sequence":"additional","affiliation":[]},{"given":"Heenaben","family":"Patel","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Farjo","sequence":"additional","affiliation":[]},{"given":"Brijesh","family":"Patel","sequence":"additional","affiliation":[]},{"given":"Partho P.","family":"Sengupta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"467_CR1","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1056\/NEJMoa2015432","volume":"383","author":"M Ackermann","year":"2020","unstructured":"Ackermann, M. et al. 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Dr. Sengupta is a consultant for Kencor Health and Ultromics. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"95"}}