Learning rich medical concept representations is essential for EHR prediction.
Text-attributed knowledge graphs (TKGs) provide a natural foundation by organizing heterogeneous medical relations together with textual semantics.
However, most existing encoders process concepts uniformly across patients, despite the fact that a code's meaning and predictive value depend on patient-specific clinical context and trajectory.
Learning patient-personalized concept representations from TKGs introduces two key challenges:
- deciding how much KG context to incorporate for each observed code,
- aligning semantic information with the patient-specific relational structure.
We propose REFINE, a KG-aware budgeted LLM graph refinement framework for patient-personalized medical concept encoding.
Starting from a global TKG, REFINE constructs patient-specific temporal graphs.
A sequential reinforcement learning policy selects a personalized KG expansion budget for each observed code.
The resulting patient graph is processed by a heterogeneous GNN to capture relation-aware structural dependencies, while a frozen LLM uses graph-aware soft prompts to semantically refine concept representations.
Experiments on MIMIC-III and MIMIC-IV show that REFINE consistently improves diverse EHR backbones, outperforms strong baselines, and demonstrates robust gains across component ablation, KG selection, and data insufficiency.