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Updated: May 17, 2026
Nasal Brushing Sampling and Processing Using Digital High Speed Ciliary Videomicroscopy – Adaptation for the COVID-19 Pandemic
Published on: November 7, 2020
FLANDERS: Fast Learning COVID-19 Care System
Alberto García-Blanco1, A Giuliano Mirabella1, Esther Román-Villarán1
1Computational Health Informatics Group. Institute of Biomedicine of Seville, IBIS/Virgen del Rocio University Hospital/CSIC/University of Seville.
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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