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MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot

A retrieval-augmented generation model enhanced by knowledge graph-elicited reasoning for healthcare copilots.

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MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot

By Xuejiao Zhao, Siyan Liu, Su-Yin Yang, Chunyan Miao
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This paper proposes MedRAG, a RAG model that integrates knowledge graphs and large language models to improve diagnostic accuracy in healthcare. It constructs a hierarchical diagnostic KG and retrieves EHRs to provide more accurate decision support.

Experimental results show that MedRAG outperforms state-of-the-art models in reducing misdiagnosis rates.

Abstract

This paper proposes MedRAG, a RAG model that integrates knowledge graphs and large language models to improve diagnostic accuracy in healthcare. It constructs a hierarchical diagnostic KG and retrieves EHRs to provide more accurate decision support. Experimental results show that MedRAG outperforms state-of-the-art models in reducing misdiagnosis rates.

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medraghealthcare copilotknowledge graph elicited reasoningretrieval augmented generationKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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