Summary of Autord: An Automatic and End-to-end System For Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models, by Lang Cao et al.
AutoRD: An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontologies-enhanced Large Language Models
by Lang Cao, Jimeng Sun, Adam Cross
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to address the challenge of limited research focus on rare diseases by developing an end-to-end system called AutoRD that automates the extraction of information from medical texts about rare diseases. The proposed system integrates structured knowledge and demonstrates superior performance in rare disease extraction tasks, surpassing common Large Language Models (LLMs) and traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This AI-powered system can help improve medical diagnosis and management by providing healthcare professionals with accurate and up-to-date information on rare diseases. The AutoRD system is designed to extract entities and their relations from medical texts, which can lead to better understanding of these complex conditions. |
