Advancing Feature Extraction in Healthcare Through the Integration of Knowledge Graphs and Large Language Models

Abstract

The exponential growth of unstructured medical data presents a unique opportunity and challenge for advancing healthcare. Traditional methods struggle to extract meaningful insights from this complex data due to its inherent noise, ambiguity, and heterogeneity. To address these limitations, we propose a novel hybrid approach that integrates Knowledge Graphs (KGs) with Large Language Models (LLMs) within a Retrieval-Augmented Generation (RAG) framework. By leveraging the structured knowledge of KGs and the contextual understanding of LLMs, we aim to improve the precision of feature extraction and disease progression modeling. Our research focuses on refining the KG representation through advanced entity extraction and relation extraction techniques, ensuring that the KG accurately captures the semantic nuances and temporal dynamics of medical data. By integrating this enhanced KG with the RAG framework, we can derive more precise and informative insights for clinical decision-making.