Fairness-Aware Graph Neural Networks for ICU Length of Stay Prediction in IoT-Enabled Environments

Fairness-Aware Graph Neural Networks for ICU Length of Stay Prediction in IoT-Enabled Environments

This study introduces a novel end-to-end framework designed to optimize patient outcomes and operational efficiency in intensive care units (ICUs) through fairness-aware and accurate length of stay (LOS) predictions within an IoT-enabled environment. Motivated by the concept of “fairness...

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Bibliographic Details
Main Authors: Angelos Christos Maroudis, Konstantina Karathanasopoulou, Charithea C. Stylianides, George Dimitrakopoulos, Andreas S. Panayides
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Time series analysis
graph neural networks
length of ICU stay
MIMIC dataset
Internet of Things
patient outcome prediction
Online Access:https://ieeexplore.ieee.org/document/10963712/
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