Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
Journal:
arXiv
Published Date:
Oct 6, 2024
Abstract
Large language models (LLMs) have demonstrated significant potential in
clinical decision support. Yet LLMs still suffer from hallucinations and lack
fine-grained contextual medical knowledge, limiting their high-stake healthcare
applications such as clinical diagnosis. Traditional retrieval-augmented
generation (RAG) methods attempt to address these limitations but frequently
retrieve sparse or irrelevant information, undermining prediction accuracy. We
introduce KARE, a novel framework that integrates knowledge graph (KG)
community-level retrieval with LLM reasoning to enhance healthcare predictions.
KARE constructs a comprehensive multi-source KG by integrating biomedical
databases, clinical literature, and LLM-generated insights, and organizes it
using hierarchical graph community detection and summarization for precise and
contextually relevant information retrieval. Our key innovations include: (1) a
dense medical knowledge structuring approach enabling accurate retrieval of
relevant information; (2) a dynamic knowledge retrieval mechanism that enriches
patient contexts with focused, multi-faceted medical insights; and (3) a
reasoning-enhanced prediction framework that leverages these enriched contexts
to produce both accurate and interpretable clinical predictions. Extensive
experiments demonstrate that KARE outperforms leading models by up to
10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and
readmission predictions. In addition to its impressive prediction accuracy, our
framework leverages the reasoning capabilities of LLMs, enhancing the
trustworthiness of clinical predictions.
