Summary of Causality Extraction From Medical Text Using Large Language Models (llms), by Seethalakshmi Gopalakrishnan et al.
Causality extraction from medical text using Large Language Models (LLMs)
by Seethalakshmi Gopalakrishnan, Luciana Garbayo, Wlodek Zadrozny
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 study investigates the application of natural language models, particularly large language models, in extracting causal relationships from medical texts, focusing on Clinical Practice Guidelines (CPGs). The researchers report on the outcomes of causality extraction from CPGs for gestational diabetes, a novel contribution to the field. The experiments utilize variants of BERT (BioBERT, DistilBERT, and BERT) as well as Large Language Models (LLMs), including GPT-4 and LLAMA2. The results indicate that BioBERT outperformed other models, including LLMs, with an average F1-score of 0.72. Although GPT-4 and LLAMA2 showed similar performance, their consistency was lower. The study releases the code and an annotated corpus of causal statements within Clinical Practice Guidelines for gestational diabetes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how artificial intelligence models can help us understand the connections between medical ideas in documents called Clinical Practice Guidelines. The goal is to improve our ability to extract important information from these guidelines. The study tested several types of AI models and found that one type, called BioBERT, was the most effective. This means it can accurately identify relationships between different pieces of medical information. The research also provides a special set of labeled examples (data) for others to use. |
Keywords
» Artificial intelligence » Bert » F1 score » Gpt
