Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Evidence-based medicine (EBM) plays a crucial role in the application of
large language models (LLMs) in healthcare, as it provides reliable support for
medical decision-making processes. Although it benefits from current
retrieval-augmented generation~(RAG) technologies, it still faces two
significant challenges: the collection of dispersed evidence and the efficient
organization of this evidence to support the complex queries necessary for EBM.
To tackle these issues, we propose using LLMs to gather scattered evidence from
multiple sources and present a knowledge hypergraph-based evidence management
model to integrate these evidence while capturing intricate relationships.
Furthermore, to better support complex queries, we have developed an
Importance-Driven Evidence Prioritization (IDEP) algorithm that utilizes the
LLM to generate multiple evidence features, each with an associated importance
score, which are then used to rank the evidence and produce the final retrieval
results. Experimental results from six datasets demonstrate that our approach
outperforms existing RAG techniques in application domains of interest to EBM,
such as medical quizzing, hallucination detection, and decision support.
Testsets and the constructed knowledge graph can be accessed at
\href{https://drive.google.com/file/d/1WJ9QTokK3MdkjEmwuFQxwH96j_Byawj_/view?usp=drive_link}{https://drive.google.com/rag4ebm}.
