REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation

Researchers propose REFINE, a KG-aware budgeted LLM graph refinement framework that learns patient-personalized medical concept representations from text-attributed knowledge graphs, using a sequential reinforcement learning policy to select a personalized KG expansion budget for each observed code and a frozen LLM with graph-aware soft prompts for semantic refinement. Experiments on MIMIC-III and MIMIC-IV show REFINE consistently improves diverse EHR backbones and outperforms strong baselines across component ablation, KG selection, and data insufficiency.

arXiv:2609.04415v1 Announce Type: new Abstract: 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: 1 deciding how much KG context to incorporate for each observed code, and 2 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.