Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning

Pedro Vieira, Eva Maia, Isabel Praça

2025

Abstract

Managing multiple patients in an Intensive Care Unit (ICU) can be extremely challenging. By predicting patient mortality, healthcare professionals can provide more efficient treatment and manage resources more effectively. This allows for more precise and useful interventions, potentially preventing fatalities. Although artificial intelligence (AI) is making significant advancements in this field, traditional Machine Learning (ML) continues to be the most widely used AI method, though it raises concerns about data security in collaborative environments. Since ensuring the safe handling of patients’ private data is crucial, Federated Learning (FL) has emerged as a viable alternative. Its intrinsic characteristics offer a valuable solution for training predictive models securely, as raw data does not need to be shared between participants. In this study, FL was used to develop models capable of predicting ICU patient mortality while protecting data privacy. Using data from the MIMIC-IV dataset, the most accurate model achieved an accuracy of 0.886, a recall of 0.817, and a specificity of 0.965, surpassing all the analyzed studies. A comparison between FL and traditional ML approaches revealed similar performance results. Moreover, three FL aggregation algorithms were evaluated, a less common focus in this area of research. Federated Averaging performed best with some classifiers, while delivering results comparable to FederAdagrad and FedAdam with others. In conclusion, the findings demonstrate that FL can be as effective as traditional ML for mortality prediction, with the added benefit of enhanced data privacy.

Paper Citation

in Harvard Style

Vieira P., Maia E. and Praça I. (2025). Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 87-95. DOI: 10.5220/0013120600003911

in Bibtex Style

@conference{healthinf25,
author={Pedro Vieira and Eva Maia and Isabel Praça},
title={Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={87-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013120600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Privacy-Preserving Mortality Prediction in ICUs Using Federated Learning
SN - 978-989-758-731-3
AU - Vieira P.
AU - Maia E.
AU - Praça I.
PY - 2025
SP - 87
EP - 95
DO - 10.5220/0013120600003911
PB - SciTePress