{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T04:04:28Z","timestamp":1747454668660,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685960","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,15]]},"abstract":"<jats:p>The COVID-19 pandemic highlighted the complexities of diagnosing and managing acute Respiratory Failure (RF). Early prediction of RF remains a key challenge, with no established tools currently available. This study developed a machine learning model to predict RF in hospitalised COVID-19 patients, using structured data (demographic and clinical variables) and clinical reports processed through Natural Language Processing. Early results show an AUC-ROC of 0.856 and an accuracy of 76.5\u2216% with a Random Forest model, demonstrating the potential of AI to enhance early prediction of patient outcomes in the context of RF.<\/jats:p>","DOI":"10.3233\/shti250411","type":"book-chapter","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:56:13Z","timestamp":1747385773000},"source":"Crossref","is-referenced-by-count":0,"title":["FLANDERS: Fast Learning COVID-19 Care System"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4506-7919","authenticated-orcid":false,"given":"Alberto","family":"Garc\u00eda-Blanco","sequence":"first","affiliation":[{"name":"Computational Health Informatics Group. Institute of Biomedicine of Seville, IBIS\/Virgen del Rocio University Hospital\/CSIC\/University of Seville"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3210-1546","authenticated-orcid":false,"given":"A. Giuliano","family":"Mirabella","sequence":"additional","affiliation":[{"name":"Computational Health Informatics Group. Institute of Biomedicine of Seville, IBIS\/Virgen del Rocio University Hospital\/CSIC\/University of Seville"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8360-4704","authenticated-orcid":false,"given":"Esther","family":"Rom\u00e1n-Villar\u00e1n","sequence":"additional","affiliation":[{"name":"Computational Health Informatics Group. Institute of Biomedicine of Seville, IBIS\/Virgen del Rocio University Hospital\/CSIC\/University of Seville"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2609-575X","authenticated-orcid":false,"given":"Carlos Luis","family":"Parra-Calder\u00f3n","sequence":"additional","affiliation":[{"name":"Computational Health Informatics Group. Institute of Biomedicine of Seville, IBIS\/Virgen del Rocio University Hospital\/CSIC\/University of Seville"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Intelligent Health Systems \u2013 From Technology to Data and Knowledge"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T08:56:14Z","timestamp":1747385774000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"ISBN":["9781643685960"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250411","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]}}}