面向脂肪肝预测的具备以人为中心解释性的半监督图表示学习
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease
AI总结：
针对脂肪肝预测中临床标记数据不足的问题，提出基于图神经网络的半监督图表示学习方法，构建受试者相似图挖掘风险模式，结合可解释GNN提供以人为中心的个性化解释，提升临床可用性。
摘要：
针对临床场景中标记数据有限（尤其是脂肪肝预测领域）的挑战，本研究探索了图表示学习在半监督学习框架下的应用潜力。该方法利用图神经网络（GNNs）构建受试者相似图，从体检数据中识别风险模式。研究证实，即便标记样本极少，各类GNN方法在该场景下仍具备有效性。本方法的核心是通过可解释GNN引入以人为中心的解释，提供个性化特征重要性评分，以提升可解释性与临床相关性，凸显了该方法在推动医疗实践发展方面的潜力，重点聚焦图表示学习与以人为中心的解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
