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.

翻译：针对临床环境中标记数据有限（尤其是在脂肪肝疾病预测中）的挑战，本研究探索了在半监督学习框架下利用图表示学习的潜力。通过图神经网络，我们构建了受试者相似性图，从健康体检数据中识别风险模式。研究证明了多种图神经网络方法在此场景下的有效性，即便在标记样本极少时仍表现良好。方法的核心在于通过可解释图神经网络融入以人为中心的解释机制，提供个性化特征重要性评分以增强可解释性和临床相关性，从而凸显了该方法在推动医疗实践中聚焦图表示学习与以人为中心解释的潜力。