Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation

TL;DR

This paper introduces a novel approach that combines UMLS knowledge base with GPT models to enhance document-level clinical entity and relation extraction, outperforming existing methods like RAG.

Contribution

The work presents a knowledge-guided generation framework that leverages UMLS concepts to improve clinical information extraction at the document level.

Findings

UMLS-guided prompts improve extraction accuracy.

The approach outperforms standard RAG techniques.

Knowledge integration enhances GPT's clinical understanding.

Abstract

Generative pre-trained transformer (GPT) models have shown promise in clinical entity and relation extraction tasks because of their precise extraction and contextual understanding capability. In this work, we further leverage the Unified Medical Language System (UMLS) knowledge base to accurately identify medical concepts and improve clinical entity and relation extraction at the document level. Our framework selects UMLS concepts relevant to the text and combines them with prompts to guide language models in extracting entities. Our experiments demonstrate that this initial concept mapping and the inclusion of these mapped concepts in the prompts improves extraction results compared to few-shot extraction tasks on generic language models that do not leverage UMLS. Further, our results show that this approach is more effective than the standard Retrieval Augmented Generation (RAG) technique, where retrieved data is compared with prompt embeddings to generate results. Overall, we find that integrating UMLS concepts with GPT models significantly improves entity and relation identification, outperforming the baseline and RAG models. By combining the precise concept mapping capability of knowledge-based approaches like UMLS with the contextual understanding capability of GPT, our method highlights the potential of these approaches in specialized domains like healthcare.