用超图与Transformer结合，更准确预测疾病诊断。
Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Records
- 用超图建模医疗代码间高阶关系，突破传统成对连接限制。
- 在真实EHR数据上，诊断预测准确率超越现有最优模型。
- 适合从事医疗AI、电子病历分析的研究者与临床决策支持系统开发者。
电子健康记录（EHR）通过标准化医疗编码系统地组织患者健康数据，是预测建模的宝贵资源。图神经网络（GNN）在建模医疗编码间交互方面表现有效，但现有方法存在两大局限：其一，依赖成对关系，无法捕捉临床数据中的固有高阶依赖；其二，局部消息传递机制限制了表示能力。为此，本文提出一种新型结构感知超图变换器（SHGT）框架，包含三个核心思想：其一，采用超图结构编码器捕获医疗代码间的高阶交互；其二，融合Transformer架构对整个超图进行全局推理；其三，设计定制化损失函数，通过超图重构任务保持原始结构。在真实EHR数据集上的实验表明，所提SHGT在诊断预测任务中优于现有最先进模型。
原文摘要 · Abstract (English)
Electronic Health Records (EHR) systematically organize patient health data through standardized medical codes, serving as a comprehensive and invaluable source for predictive modeling. Graph neural networks (GNNs) have demonstrated effectiveness in modeling interactions between medical codes within EHR. However, existing GNN-based methods are inadequate due to: a) their reliance on pairwise relations fails to capture the inherent higher-order dependencies in clinical data, and b) the localized message-passing scheme limits representation power. To address these issues, this paper proposes a novel Structure-aware HyperGraph Transformer (SHGT) framework following three-fold ideas: a) employing a hypergraph structural encoder to capture higher-order interactions among medical codes, b) integrating the Transformer architecture to reason over the entire hypergraph, and c) designing a tailored loss function incorporating hypergraph reconstruction to preserve the hypergraph's original structure. Experiments on real-world EHR datasets demonstrate that the proposed SHGT outperforms existing state-of-the-art models on diagnosis prediction.
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