Summary of Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation, by Pablo Messina et al.
Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation
by Pablo Messina, René Vidal, Denis Parra, Álvaro Soto, Vladimir Araujo
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The proposed two-stage framework, comprising a Fact Extractor and a Fact Encoder (CXRFE), advances representation learning in medicine by extracting high-quality factual statements from radiology reports. The Fact Extractor leverages large language models to identify factual statements from well-curated datasets, while the CXRFE fine-tunes BERT with objective functions designed to improve its representations using extracted facts. A new embedding-based metric (CXRFEScore) is introduced for evaluating chest X-ray text generation systems, leveraging both stages of the approach. Experimental results show that the framework outperforms state-of-the-art methods in tasks like sentence ranking, natural language inference, and label extraction from radiology reports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to help computers understand medical texts better. It’s hard to get good training data for this kind of task because doctors don’t have time to write lots of examples. To solve this problem, the researchers created a two-step system that first finds important facts in medical reports and then uses those facts to make the computer better at understanding similar texts. They also came up with a new way to measure how well the system is working. The results show that their approach works really well for tasks like finding the main idea of a sentence or understanding the relationship between two sentences. |
Keywords
» Artificial intelligence » Bert » Embedding » Encoder » Inference » Representation learning » Text generation
