{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:12:57Z","timestamp":1780362777526,"version":"3.54.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Importance<\/jats:title>\n                <jats:p>Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30\u00a0days of admission is useful for delivering appropriate clinical care and optimizing resource allocation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Design, setting, and participants<\/jats:title>\n                <jats:p>We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest\u2019s feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Main outcomes and measures<\/jats:title>\n                <jats:p>Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7\u00a0years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30\u00a0days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/faculty.tamuc.edu\/mmete\/covid-risk.html\">https:\/\/faculty.tamuc.edu\/mmete\/covid-risk.html<\/jats:ext-link>.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions and relevance<\/jats:title>\n                <jats:p>In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02132-4","type":"journal-article","created":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T14:03:00Z","timestamp":1678197780000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Machine learning prediction for COVID-19 disease severity at hospital admission"],"prefix":"10.1186","volume":"23","author":[{"given":"Ganesh","family":"Raman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bilal","family":"Ashraf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuf Kemal","family":"Demir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corey D.","family":"Kershaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sreekanth","family":"Cheruku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"Atis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahsen","family":"Atis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa","family":"Atar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weina","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taha","family":"Bat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mutlu","family":"Mete","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"2132_CR1","unstructured":"\u201cCenters for Disease Control and Prevention,\u201d COVID Data Tracker Cent. 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Data is available on request from the authors. The study protocol was reviewed and an informed consent waiver was obtained from the Institutional Review Board of University of Texas Southwestern Medical Center on August 18, 2020 (IRB No: STU-2020-0832). Authors confirm that study was conducted in accordance with the Declaration of Helsinki We also confirm that all experimental protocols were approved by Institutional Review Board of University of Texas Southwestern Medical Center.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"46"}}