LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
Researchers propose using large language models as graph structure refiners to improve EEG-based seizure detection by identifying and removing redundant connections in noisy neural signal data. A two-stage framework combining Transformer-based edge prediction with LLM validation demonstrates improved accuracy and more interpretable graph representations on the TUSZ dataset.