AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models

TL;DR

AutoRD is an end-to-end system that leverages ontologies-enhanced large language models to automate the extraction of rare disease knowledge from medical texts, integrating structured knowledge for improved accuracy.

Contribution

The paper introduces AutoRD, a novel system that combines ontologies with large language models to enhance rare disease information extraction and knowledge graph construction.

Findings

AutoRD outperforms traditional methods in rare disease extraction tasks.

AutoRD effectively integrates structured knowledge for improved accuracy.

Experimental results demonstrate superior performance over common LLMs.

Abstract

Rare diseases affect millions worldwide but often face limited research focus due to their low prevalence. This results in prolonged diagnoses and a lack of approved therapies. Recent advancements in Large Language Models (LLMs) have shown promise in automating the extraction of medical information, offering potential to improve medical diagnosis and management. However, most LLMs lack professional medical knowledge, especially concerning rare diseases, and struggle to handle the latest rare disease information. They also cannot effectively manage rare disease data and are not directly suitable for diagnosis and management tasks. Our objective is to create an end-to-end system called AutoRD, which automates the extraction of information from medical texts about rare diseases, focusing on entities and their relations. AutoRD integrates up-to-date structured knowledge and demonstrates superior performance in rare disease extraction tasks. We conduct various experiments to evaluate AutoRD's performance, aiming to surpass common LLMs and traditional methods.