{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T03:07:40Z","timestamp":1769742460496,"version":"3.49.0"},"reference-count":46,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2023,3,7]],"date-time":"2023-03-07T00:00:00Z","timestamp":1678147200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Francesco Balsano Foundation","award":["FFB grant 2020\/2021"],"award-info":[{"award-number":["FFB grant 2020\/2021"]}]},{"name":"The Internal Review Board","award":["IRB 06\/2021"],"award-info":[{"award-number":["IRB 06\/2021"]}]},{"name":"The Internal Review Board","award":["Protocol number 28958"],"award-info":[{"award-number":["Protocol number 28958"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2\/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19.<\/jats:p>","DOI":"10.1515\/jib-2022-0047","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T20:20:28Z","timestamp":1678134028000},"source":"Crossref","is-referenced-by-count":8,"title":["Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients"],"prefix":"10.1515","volume":"20","author":[{"given":"Nicol\u00f2","family":"Casano","sequence":"first","affiliation":[{"name":"School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA , University of L\u2019Aquila , L\u2019Aquila , Italy"}]},{"given":"Silvano Junior","family":"Santini","sequence":"additional","affiliation":[{"name":"School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA , University of L\u2019Aquila , L\u2019Aquila , Italy"},{"name":"Francesco Balsano Foundation , Via Giovanni Battista Martini 6, 00198 , Rome , Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6975-8958","authenticated-orcid":false,"given":"Pierpaolo","family":"Vittorini","sequence":"additional","affiliation":[{"name":"School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA , University of L\u2019Aquila , L\u2019Aquila , Italy"}]},{"given":"Gaia","family":"Sinatti","sequence":"additional","affiliation":[{"name":"School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA , University of L\u2019Aquila , L\u2019Aquila , Italy"},{"name":"Francesco Balsano Foundation , Via Giovanni Battista Martini 6, 00198 , Rome , Italy"}]},{"given":"Paolo","family":"Carducci","sequence":"additional","affiliation":[{"name":"School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA , University of L\u2019Aquila , L\u2019Aquila , Italy"}]},{"given":"Claudio Maria","family":"Mastroianni","sequence":"additional","affiliation":[{"name":"Department of Public Health and Infectious Diseases , \u201cSapienza\u201d University of Rome, Policlinico Umberto I Hospital , Rome , Italy"}]},{"given":"Maria Rosa","family":"Ciardi","sequence":"additional","affiliation":[{"name":"Department of Public Health and Infectious Diseases , \u201cSapienza\u201d University of Rome, Policlinico Umberto I Hospital , Rome , Italy"}]},{"given":"Patrizia","family":"Pasculli","sequence":"additional","affiliation":[{"name":"Department of Public Health and Infectious Diseases , \u201cSapienza\u201d University of Rome, Policlinico Umberto I Hospital , Rome , Italy"}]},{"given":"Emiliano","family":"Petrucci","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Intensive Care and Pain Treatment , University of L\u2019Aquila , L\u2019Aquila , Italy"}]},{"given":"Franco","family":"Marinangeli","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Intensive Care and Pain Treatment , University of L\u2019Aquila , L\u2019Aquila , Italy"}]},{"given":"Clara","family":"Balsano","sequence":"additional","affiliation":[{"name":"School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA , University of L\u2019Aquila , L\u2019Aquila , Italy"},{"name":"Francesco Balsano Foundation , Via Giovanni Battista Martini 6, 00198 , Rome , Italy"}]}],"member":"374","published-online":{"date-parts":[[2023,3,7]]},"reference":[{"key":"2023073113214252060_j_jib-2022-0047_ref_001","unstructured":"World Health Organization. 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