导入所需内容：学习何时以及如何用外部知识增强电子健康记录图
Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge
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中文总结 AI 辅助
针对EHR纵向预测的稀疏性与不规则性问题，提出基于强化学习的动态拓扑增强框架ReTA，可按需选择增强操作，在MIMIC数据集相关任务中表现优于基线且具可迁移性与可解释性
中文摘要 AI 辅助
电子健康记录（EHR）的纵向预测受限于患者轨迹的稀疏性和不规则性，利用外部知识图谱（KG）进行知识增强是缓解这些问题的有效途径。然而，现有大多数方法采用固定的、与上下文无关的拓扑增强方式，无论患者状态如何变化，都添加相同的KG节点和边。本文提出ReTA，这是一种基于强化学习的动态拓扑增强框架，将KG导入视为按就诊、受预算约束的策略。ReTA首先构建基于KG的模板的离线精炼池，然后学习策略，为每次就诊从三个选项中选择一个增强操作：软导入（Soft Import），在不修改图拓扑的情况下丰富节点特征；硬导入（Hard Import），将紧凑的KG子图嫁接至就诊图以创建消息传递捷径；弃权（Skip），当基础编码器已足够自信时，不对就诊进行增强。为稳定学习，ReTA采用解耦编码器，在独立通道中处理语义和结构信号，并通过自适应门控融合它们。在MIMIC-III和MIMIC-IV数据集上，针对诊断预测、死亡率和再入院任务的实验表明，ReTA在保持高效的同时，始终优于强大的基线方法，可跨数据集和知识图谱迁移，且能产生可解释的增强模式。在稀疏监督下的稳健增益凸显了ReTA动态导入知识决策的优势，在提升准确率的同时控制了成本。
英文摘要
Longitudinal prediction from electronic health records (EHRs) is limited by the sparsity and irregularity in patient trajectories, and knowledge augmentation with external knowledge graphs (KGs) offers a promising way to alleviate these issues. However, most existing methods perform fixed, context-agnostic topology augmentation by adding the same KG nodes and edges regardless of a patient's evolving state. We propose ReTA, a Reinforcement learning-based dynamic Topology Augmentation framework that casts KG import as a per-visit, budget-aware policy. ReTA first constructs an offline refined pool of KG-grounded templates, then learns a policy to select one augment action per visit from three options: Soft Import, which enriches node features without modifying graph topology, Hard Import, which grafts a compact KG subgraph onto the visit graph to create message-passing shortcuts, and Skip, which leaves the visit unaugmented when the base encoder is already confident. To stabilize learning, ReTA employs a decoupled encoder that processes semantic and structural signals in separate channels and fuses them via adaptive gating. Experiments on MIMIC-III and MIMIC-IV across diagnosis prediction, mortality, and readmission show that ReTA consistently outperforms strong baselines while remaining efficient, transfers across datasets and knowledge graphs, and yields interpretable augmentation patterns. The robust gains under sparse supervision highlight the advantage of ReTA's dynamic decision to import knowledge, boosting accuracy while curbing costs.
发表机构
- University of Kansas（堪萨斯大学）
- University of Florida（佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。
