A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units

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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Publication Details

Journal
Studies in health technology and informatics
Published
2025-05-15
DOI
https://doi.org/10.3233/shti250311
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units

Johanna Schwinn, Ludwig Christian Hinske, Seyedmostafa Sheikhalishahi, Mathias Kaspar et al.
Studies in health technology and informatics
Machine Learning in Healthcare
article

A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units

Johanna Schwinn, Ludwig Christian Hinske, Seyedmostafa Sheikhalishahi, Mathias Kaspar, Matthaeus Morhart
article en

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.

Studies in health technology and informaticsVol. 327
University Hospital Augsburg (DE)
Decent work and economic growth
Openalex Percentile: Top 81%
Machine Learning in Healthcare
DOIOpen Access PDFOpenAlex
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A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units — Johanna Schwinn, Ludwig Christian Hinske, et al. · Studies in health technology and informatics (2025) | TGRS Research Map | TGRS