Alberto García-Blanco, A Giuliano Mirabella, Esther Román-Villarán, Carlos Luis Parra-Calderón
{"title":"FLANDERS: Fast Learning COVID-19 Care System.","authors":"Alberto García-Blanco, A Giuliano Mirabella, Esther Román-Villarán, Carlos Luis Parra-Calderón","doi":"10.3233/SHTI250411","DOIUrl":null,"url":null,"abstract":"<p><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∖% with a Random Forest model, demonstrating the potential of AI to enhance early prediction of patient outcomes in the context of RF.</p>","PeriodicalId":94357,"journal":{"name":"Studies in health technology and informatics","volume":"327 ","pages":"599-600"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Studies in health technology and informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/SHTI250411","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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∖% with a Random Forest model, demonstrating the potential of AI to enhance early prediction of patient outcomes in the context of RF.
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