Summary of Document-level Clinical Entity and Relation Extraction Via Knowledge Base-guided Generation, by Kriti Bhattarai et al.
Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation
by Kriti Bhattarai, Inez Y. Oh, Zachary B. Abrams, Albert M. Lai
First submitted to arxiv on: 13 Jul 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 This research explores the application of generative pre-trained transformer (GPT) models in clinical entity and relation extraction tasks. By leveraging the Unified Medical Language System (UMLS) knowledge base, the study aims to improve the precision and contextual understanding of GPT models in identifying medical concepts at the document level. The proposed framework utilizes UMLS concepts to guide language models in extracting entities, which is compared to few-shot extraction tasks on generic language models that do not utilize UMLS. The results demonstrate that this approach outperforms both the baseline and Retrieval Augmented Generation (RAG) technique, highlighting the potential of integrating UMLS concepts with GPT models in specialized domains like healthcare. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computers to help doctors better understand medical texts. It combines two ideas: using a huge dictionary of medical terms (Unified Medical Language System or UMLS) and using a type of computer program called a generative pre-trained transformer (GPT). The goal is to help the computer find important medical concepts in text, like what medicines are being talked about or who is involved. The researchers tried different approaches and found that combining UMLS with GPT works better than other methods. This could be very helpful for doctors and hospitals trying to understand and process large amounts of medical information. |
Keywords
» Artificial intelligence » Few shot » Gpt » Knowledge base » Precision » Rag » Retrieval augmented generation » Transformer
