FLANDERS: Fast Learning COVID-19 Care System

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.