[HTML][HTML] Survey of Medical Applications of Federated Learning
Objectives Medical artificial intelligence (AI) has recently attracted considerable attention.
However, training medical AI models is challenging due to privacy-protection regulations …
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
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 …
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 …
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
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 …
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 …
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
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 …
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 …
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 …
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 …
useful for scientific advancements and new applications, but also presents privacy and …