用图卷积分析电子病历，挖掘患者数据中的复杂关系。
Medical Applications of Graph Convolutional Networks Using Electronic Health Records: A Survey
- 将病历数据构建为图结构，通过节点邻域卷积捕捉医疗关联
- 在疾病预测、风险评估等任务中展现良好性能，支持临床决策
- 适合医学人工智能研究者和临床数据挖掘人员参考
图卷积网络（GCNs）已成为电子病历（EHR）数据机器学习的有前景方法。通过构建患者数据的图表示并执行节点邻域上的卷积操作，GCNs 能够捕捉复杂的医疗关系并提取有意义的洞察，以支持医学决策。本综述全面梳理了当前将 GCN 应用于 EHR 数据的研究进展，涵盖关键医疗领域、预测任务、常用基准数据集及模型架构模式。尽管该领域尚处于起步阶段，但 GCN 在挖掘 EHR 中隐藏的复杂信息方面展现出强大潜力。文中还讨论了未来研究的挑战与机遇。
原文摘要 · Abstract (English)
Graph Convolutional Networks (GCNs) have emerged as a promising approach to machine learning on Electronic Health Records (EHRs). By constructing a graph representation of patient data and performing convolutions on neighborhoods of nodes, GCNs can capture complex relationships and extract meaningful insights to support medical decision making. This survey provides an overview of the current research in applying GCNs to EHR data. We identify the key medical domains and prediction tasks where these models are being utilized, common benchmark datasets, and architectural patterns to provide a comprehensive survey of this field. While this is a nascent area of research, GCNs demonstrate strong potential to leverage the complex information hidden in EHRs. Challenges and opportunities for future work are also discussed.
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