Work

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

Year: 2025

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250311

Authors: Johanna Schwinn, Seyedmostafa Sheikhalishahi, Matthaeus Morhart, Mathias Kaspar, Ludwig Christian Hinske

Institution: University Hospital Augsburg

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4410448052

FWCI: 4.348

Cites:

Cited by: 2

Related to: 10