Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge

2026-09-01

AI Summary

为解决电子健康记录中数据稀疏和不规则问题，提出了一种基于强化学习的动态拓扑增强框架ReTA，通过智能选择是否及如何引入外部知识图谱来优化预测性能。

Abstract

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.

Problem

• electronic health records
• knowledge augmentation
• external knowledge graphs
• patient trajectories

Innovation

• Reinforcement learning-based dynamic Topology Augmentation
• budget-aware policy
• KG-grounded templates
• Soft Import
• Hard Import