Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease
This addresses the challenge of limited labeled data in clinical settings for healthcare practitioners, though it appears incremental as it applies existing GNN methods to a specific medical domain.
This study tackled the problem of predicting fatty liver disease with limited labeled clinical data by using graph neural networks in a semi-supervised framework, demonstrating effectiveness even with minimal labeled samples and incorporating human-centric explanations for interpretability.
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.