HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompted LLMs produce none.
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Fine-tuning and data augmentation improve LLM performance on medical jargon extraction and prioritization from EHR notes, with augmented open-source models sometimes outperforming closed-source ones on 106 annotated notes.
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The Provenance Gap in Clinical AI: Evidence-Traceable Temporal Knowledge Graphs for Rare Disease Reasoning
HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompted LLMs produce none.
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Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation
Fine-tuning and data augmentation improve LLM performance on medical jargon extraction and prioritization from EHR notes, with augmented open-source models sometimes outperforming closed-source ones on 106 annotated notes.