用可微池化图变换器建模跨就诊的医疗事件动态关系
DeepJ: Graph Convolutional Transformers with Differentiable Pooling for Patient Trajectory Modeling
- 结合图卷积与注意力机制，通过可微池化捕捉跨就诊事件关联
- 在多个数据集上超越5个主流模型，提升患者风险预测准确率
- 适合临床研究者分析长期诊疗路径，辅助个性化诊疗决策
近年来，图学习在建模结构化电子健康记录（EHR）中医疗事件间复杂交互方面受到广泛关注。然而，现有基于图的方法通常采用静态方式，要么限制交互仅发生在单次就诊内，要么将所有历史就诊合并为单一快照。因此，当需要识别跨越纵向就诊、具有时间与功能相关性的关键医疗事件组时，现有方法难以同时建模跨就诊交互与时间依赖性。为此，我们提出深度患者旅程（DeepJ），一种融合可微图池化的图卷积变压器模型，能够有效捕捉就诊内与就诊间的医疗事件交互。DeepJ可识别时间与功能相关的事件群组，为患者预后预测提供关键洞察。实验表明，DeepJ显著优于五个最先进的基线模型，同时增强可解释性，展现出在改善患者风险分层方面的潜力。
原文摘要 · Abstract (English)
In recent years, graph learning has gained significant interest for modeling complex interactions among medical events in structured Electronic Health Record (EHR) data. However, existing graph-based approaches often work in a static manner, either restricting interactions within individual encounters or collapsing all historical encounters into a single snapshot. As a result, when it is necessary to identify meaningful groups of medical events spanning longitudinal encounters, existing methods are inadequate in modeling interactions cross encounters while accounting for temporal dependencies. To address this limitation, we introduce Deep Patient Journey (DeepJ), a novel graph convolutional transformer model with differentiable graph pooling to effectively capture intra-encounter and inter-encounter medical event interactions. DeepJ can identify groups of temporally and functionally related medical events, offering valuable insights into key event clusters pertinent to patient outcome prediction. DeepJ significantly outperformed five state-of-the-art baseline models while enhancing interpretability, demonstrating its potential for improved patient risk stratification.
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