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Neural gradient boosting in federated learning for hemodynamic instability prediction: towards a distributed and

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Federated learning (FL) now supports decision tree models for predicting patient hemodynamic instability. This approach enhances privacy and achieves comparable accuracy to centralized methods in intensive care units (ICUs).

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Machine Learning

Background:

  • Federated learning (FL) addresses data privacy and security challenges in healthcare by enabling collaborative model training without data sharing.
  • Current FL architectures primarily support neural networks, lacking scalability and efficient integration of decision tree-based models, especially for sensitive clinical data.
  • Decision tree models are state-of-the-art for tabular clinical data but face performance and security issues when implemented in FL due to encryption costs.

Purpose of the Study:

  • To develop and evaluate a federated learning pipeline that enables distributed gradient boosting for predicting hemodynamic instability in intensive care unit (ICU) patients.
  • To enhance FL architectures to support decision tree models, overcoming limitations of existing neural network-centric approaches.
  • To assess the performance of the proposed FL approach against a centralized setup using a multi-hospital clinical dataset.

Main Methods:

  • Implemented a federated learning pipeline incorporating distributed gradient boosting for hemodynamic instability prediction.
  • Designed the pipeline to support both neural-based boosting models and conventional neural networks, enabling decision tree integration within FL.
  • Utilized a clinical dataset from 25 hospitals, derived from the Philips eICU database, for model training and validation.

Main Results:

  • Achieved comparable performance in accuracy, precision, recall, and F1 score for hemodynamic instability detection using FL versus a centralized setup.
  • Demonstrated the feasibility of a scalable FL approach for analyzing ICU data while preserving patient privacy.
  • Validated the effectiveness of integrating decision tree models into FL for clinical classification tasks.

Conclusions:

  • Federated learning can be effectively scaled for detecting hemodynamic instability in ICU patients, maintaining data privacy.
  • The developed FL pipeline successfully integrates decision tree models, offering advantages over traditional neural network-based FL architectures for tabular clinical data.
  • This work paves the way for broader adoption of advanced machine learning techniques in privacy-preserving healthcare analytics.