Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要