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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    As predicting the trajectory of COVID-19 is challenging, machine learning models could assist physicians in identifying high-risk individuals. This study compares the performance of 18 machine learning algorithms for predicting ICU admission and mortality among COVID-19 patients. Using COVID-19 patient data from the Mass General Brigham (MGB) Healthcare database, we developed and internally validated models using patients presenting to the Emergency Department (ED) between March-April 2020 (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20093597) and further validated them using temporally distinct individuals who presented to the ED between May-August 2020 (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20091711). We show that ensemble-based models perform better than other model types at predicting both 5-day ICU admission and 28-day mortality from COVID-19. CRP, LDH, and O\n                    <jats:sub>2<\/jats:sub>\n                    saturation were important for ICU admission models whereas eGFR &lt;60\u2009ml\/min\/1.73\u2009m\n                    <jats:sup>2<\/jats:sup>\n                    , and neutrophil and lymphocyte percentages were the most important variables for predicting mortality. Implementing such models could help in clinical decision-making for future infectious disease outbreaks including COVID-19.\n                  <\/jats:p>","DOI":"10.1038\/s41746-021-00456-x","type":"journal-article","created":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T06:02:50Z","timestamp":1621576970000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":141,"title":["Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5937-1880","authenticated-orcid":false,"given":"Sonu","family":"Subudhi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9497-6327","authenticated-orcid":false,"given":"Ashish","family":"Verma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6812-1464","authenticated-orcid":false,"given":"Ankit B.","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. 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R.K.J. received honorarium from Amgen; consultant fees from Chugai, Elpis, Merck, Ophthotech, Pfizer, SPARC, SynDevRx, XTuit; owns equity in Accurius, Enlight, Ophthotech, SynDevRx; and serves on the Boards of Trustees of Tekla Healthcare Investors, Tekla Life Sciences Investors, Tekla Healthcare Opportunities Fund, Tekla World Healthcare Fund; and received a grant from Boehringer Ingelheim. Neither any reagent nor any funding from these organizations was used in this study. Other coauthors have no conflict of interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"87"}}