Predicting blood transfusion after ICU admission in five databases: A comparison of three machine learning paradigms

Objective The objective of this retrospective study is to compare three learning approaches for blood transfusion (BT) prediction after intensive care unit (ICU) admission: local learning (LL), federated learning (FL), and centralized learning (CL) across five ICU databases (eICU Collaborative Research Database, Medical Information Mart for Intensive Care IV, High-Resolution Intensive Care Unit Dataset, Amsterdam University Medical Center Database, University Hospital of Augsburg). Methods As machine learning model we used XGBoost and included 15 clinical variables. The prediction task consists of a 3-h observation window, followed by a 2-h prediction window. We evaluated the models using internal and external validation with area under the receiver-operator curve, area under the precision-recall curve, PPV, Brier score and F1 score. Results CL consistently outperformed FL and LL in both internal validation (AUPRC range: 0.73–0.95 (CL) vs 0.63–0.96 (FL) and 0.69–0.96 (LL)) and external validation (AUPRC range: 0.61–0.89 (CL) vs 0.45–0.91 (FL) and 0.37–0.90 (LL)). FL showed variable performance across datasets. Conclusions The complexity of the multivariable clinical prediction of BTs may create substantial challenges for FL effectiveness, particularly under high data heterogeneity conditions that are common in healthcare.