{"paper_id":"f0724fec-82c0-46bc-bf09-6942ec324f4d","body_text":"Machine learning for predicting clinical outcomes in emergency department patients with acute respiratory infections: A scoping review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Machine learning for predicting clinical outcomes in emergency department patients with acute respiratory infections: A scoping review Maria Christina Mallet, Immanuel Redah, Yesmine G. Sahnoun, Hermann Nabi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9521303/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Machine learning approaches, including deep learning, are increasingly applied in healthcare, particularly for developing models that support clinical prediction and decision-making. In this context, acute respiratory infections represent a critical clinical area where timely and accurate risk stratification is essential, particularly in the emergency department (ED). This scoping review aims to systematically map and describe how machine learning approaches have been used to predict clinical outcomes in patients presenting with acute respiratory infections to the ED. We searched five databases (PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library) from inception up to July 9th, 2025, and included 52 studies. Most studies were retrospective in design (87%) and were published after 2020 (88%). Three-quarters focused on COVID-19, and the majority included adults only (69%). The largest share of studies (38%) used data originating from the United States. While most studies reported either sex or gender (88%), none reported both, and over a third (37%) used these two distinct constructs interchangeably. Race and/or ethnicity were reported in only 29% of the studies. Mortality was the most frequently predicted outcome (40%). Machine learning methods most commonly used were random forests (40%) and extreme gradient boosting (29%). Deep learning approaches were also commonly used, particularly convolutional neural networks (31%). In most studies (90%), prediction models relied on laboratory or radiological data, which are unavailable at initial triage and are typically obtained later in the ED pathway. Although machine learning-based models showed adequate performance overall, only a few studies compared them to clinical experts or traditional decision tools (21%), and/or performed external validation (13%). Overall, the models reviewed here have limited clinical utility and generalizability. Future studies should broaden the scope beyond COVID-19 to include other acute respiratory infections, develop models with data that are readily available at triage, and incorporate more diverse populations to enhance inclusivity and fairness. Infectious Diseases Critical Care & Emergency Medicine Artificial Intelligence and Machine Learning Machine learning acute respiratory infections emergency department triage prediction model Full Text Additional Declarations The authors declare no competing interests. Supplementary Files AppendixS1Supplementarytablesandfigurespreprint20260427.docx Appendix S1: Supplementary tables and figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9521303\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Systematic Review\",\"associatedPublications\":[],\"authors\":[{\"id\":631248763,\"identity\":\"b66a4c88-2002-42db-aa59-40827d7e2a4f\",\"order_by\":0,\"name\":\"Maria Christina Mallet\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0000-0002-3633-8017\",\"institution\":\"Infectious and immune diseases Research program, CHU de Québec–Université Laval Research center, Québec, Québec, Canada\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Maria\",\"middleName\":\"Christina\",\"lastName\":\"Mallet\",\"suffix\":\"\"},{\"id\":631253533,\"identity\":\"046a6dcc-e995-48b9-9bf4-ca64d81a59c1\",\"order_by\":1,\"name\":\"Immanuel Redah\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Infectious and immune diseases Research program, CHU de Québec–Université Laval Research center, Québec, Québec, Canada\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Immanuel\",\"middleName\":\"\",\"lastName\":\"Redah\",\"suffix\":\"\"},{\"id\":631253534,\"identity\":\"e0a66b36-b4d7-4331-83ea-881eeef4ce6c\",\"order_by\":2,\"name\":\"Yesmine G. 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In this context, acute respiratory infections represent a critical clinical area where timely and accurate risk stratification is essential, particularly in the emergency department (ED). This scoping review aims to systematically map and describe how machine learning approaches have been used to predict clinical outcomes in patients presenting with acute respiratory infections to the ED. We searched five databases (PubMed, Embase, Web of Science, CINAHL, and the Cochrane Library) from inception up to July 9th, 2025, and included 52 studies. Most studies were retrospective in design (87%) and were published after 2020 (88%). Three-quarters focused on COVID-19, and the majority included adults only (69%). The largest share of studies (38%) used data originating from the United States. While most studies reported either sex or gender (88%), none reported both, and over a third (37%) used these two distinct constructs interchangeably. 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