A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units
Johanna Schwinn, Seyedmostafa Sheikhalishahi, Matthaeus Morhart, Mathias Kaspar, Ludwig Christian Hinske · Studies in health technology and informatics · 2025
Accurate prediction of blood transfusion requirements is crucial for patient outcomes and resource management in clinical settings. We developed a machine learning model using XGBoost to predict the need for a blood transfusion 2 hours in advance based on up to 7 hours of prior data from two large databases, MIMIC-IV and eICU-CRD. Our federated model showed promising results, with F1 scores of 0.72 and 0.66, respectively.