LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

📰 ArXiv cs.AI

Learn how to refine clinical graph structures using LLMs to improve EEG seizure diagnosis

advanced Published 1 May 2026

Action Steps

Apply LLMs to refine clinical graph structures in EEG data
Use graph construction methods to generate initial graph representations
Refine graph structures by removing redundant or irrelevant edges using LLMs
Evaluate the quality of refined graph representations using downstream task performance metrics
Integrate refined graph representations into EEG seizure diagnosis pipelines

Who Needs to Know This

Data scientists and neurologists can benefit from this technique to enhance representation learning in EEG seizure diagnosis

Key Insight

💡 LLMs can refine clinical graph structures to improve representation learning in EEG seizure diagnosis

Key Takeaways

Learn how to refine clinical graph structures using LLMs to improve EEG seizure diagnosis

Full Article

Title: LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis

Abstract:
arXiv:2604.28178v1 Announce Type: new Abstract: Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging. Existing graph construction methods, whether correlation-based or learning-based, often generate redundant or irrelevant edges due to the noisy nature of EEG data. This significantly impairs the quality of graph representation and limits downstream task performance. Motivated by the remarkable reas

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