Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease

TL;DR

This study explores the potential of graph representation learning within a semi-supervised learning framework by leveraging graph neural networks to identify risk patterns from health checkup data and includes the inclusion of human-centric explanations through explainable GNNs.

Abstract

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.

Figures (2)

• Figure 1: Performance analysis on the varying sizes of model depth in DIFFormer and its variations, and baseline models
• Figure 2: Heatmap displaying the distribution of importance scores from features (rows) across individuals (columns), generated from the DIFFormer-attn model.