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A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units
Johanna Schwinn1, Seyedmostafa Sheikhalishahi1, Matthaeus Morhart1
1Digital Medicine, University Hospital of Augsburg, Augsburg, Germany.
Abstract:
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.
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