LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
Jensen- shannon divergence message-passing for rich-text graph representation learning.arXiv preprint arXiv:2512.20094
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SHIELD dataset and distilled DeBERTa v3 model achieve 0.88 micro precision and 0.86 recall on PHI de-identification while matching teacher performance on structured categories.
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LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis
LLM-based refinement of edges in transformer-constructed EEG graphs improves seizure detection accuracy and produces cleaner, more interpretable structures on the TUSZ dataset.
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SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
SHIELD dataset and distilled DeBERTa v3 model achieve 0.88 micro precision and 0.86 recall on PHI de-identification while matching teacher performance on structured categories.