Semi-Supervised Graph Representation Learning with Human-centric
  Explanation for Predicting Fatty Liver Disease
So Yeon Kim Sehee Wang Eun Kyung Choe
Addressing the challenge of limited labeled data in clinical settings,
particularly in the prediction of fatty liver disease, this study explores the
potential of graph representation learning within a semi-supervised learning
framework. Leveraging graph neural networks (GNNs), our approach constructs a
subject similarity graph to identify risk patterns from health checkup data.
The effectiveness of various GNN approaches in this context is demonstrated,
even with minimal labeled samples. Central to our methodology is the inclusion
of human-centric explanations through explainable GNNs, providing personalized
feature importance scores for enhanced interpretability and clinical relevance,
thereby underscoring the potential of our approach in advancing healthcare
practices with a keen focus on graph representation learning and human-centric
explanation.