REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation
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 ...
| Authors | Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Dongjie Wang et al. |
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| Primary category | cs.LG |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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