[HTML][HTML] Survey of Medical Applications of Federated Learning

G Choi, WC Cha, SU Lee… - Healthcare Informatics …, 2024 - synapse.koreamed.org
Objectives Medical artificial intelligence (AI) has recently attracted considerable attention.
However, training medical AI models is challenging due to privacy-protection regulations …

FedEYE: A scalable and flexible end-to-end federated learning platform for ophthalmology

B Yan, D Cao, X Jiang, Y Chen, W Dai, F Dong… - Patterns, 2024 - cell.com
Data-driven machine learning, as a promising approach, possesses the capability to build
high-quality, exact, and robust models from ophthalmic medical data. Ophthalmic medical …

Early prediction of the risk of ICU mortality with Deep Federated Learning

K Randl, NL Armengol… - 2023 IEEE 36th …, 2023 - ieeexplore.ieee.org
Intensive Care Units usually carry patients with a serious risk of mortality. Recent research
has shown the ability of Machine Learning to indicate the patients' mortality risk and point …

Explainable Deep Contrastive Federated Learning System for Early Prediction of Clinical Status in-Intensive Care Unit

TN Nguyen, HJ Yang, BG Kho, SR Kang… - IEEE Access, 2024 - ieeexplore.ieee.org
Early identification of patients' clinical status plays a critical role in intensive care unit (ICU)
care. The increased adoption of electronic health records (EHRs) in the ICU creates …

Federated learning on low-power Arduino Nano33 BLE Sense to predict the length of stay using a linear regression model

S Sriram, RK Hariharathmajan, A Pradeep… - Procedia Computer …, 2024 - Elsevier
Federated learning (FL) is a collaborative learning paradigm where multiple clients are used
to build the model without sharing data and preserving privacy. An FL-based linear …

FedICU: a federated learning model for reducing the medication prescription errors in intensive care units

V Pais, S Rao, B Muniyal, S Yun - Cogent Engineering, 2024 - Taylor & Francis
Patients in the Intensive care unit need remarkable observation. This unit consists of people
who are critically ill and may tend to lose their lives anytime. Healthcare professionals in …

Early ICU Mortality Prediction with Deep Federated Learning: A Real-World Scenario

A Georgoutsos, P Kerasiotis, V Kantere - Proceedings of the 35th …, 2023 - dl.acm.org
The generation of large amounts of healthcare data has motivated the use of Machine
Learning (ML) to train robust models for clinical tasks. However, limitations of local datasets …

A Comparative Analysis of Federated and Centralized Learning for SpO2 Prediction in Five Critical Care Databases

J Schwinn, S Sheikhalishahi, M Morhart… - Digital Health and …, 2024 - ebooks.iospress.nl
This study explores the potential of federated learning (FL) to develop a predictive model of
hypoxemia in intensive care unit (ICU) patients. Centralized learning (CL) and local learning …

A federated learning approach for data privacy in healthcare applications

PMR Vieira - 2024 - recipp.ipp.pt
In healthcare, actions tend to generate a vast amount of sensitive patient data, which is
useful for scientific advancements and new applications, but also presents privacy and …