Causality extraction from medical text using Large Language Models (LLMs)

This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines (CPGs). The outcomes causality extraction from Clinical Practice Guidelines for gestational diabetes are prese...

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Bibliographic Details
Published in:arXiv.org 2024-07
Main Authors: Gopalakrishnan, Seethalakshmi, Garbayo, Luciana, Zadrozny, Wlodek
Format: Article
Language:English
Subjects:
Guidelines
Large language models
Natural language processing
Online Access:Get full text
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Summary:This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines (CPGs). The outcomes causality extraction from Clinical Practice Guidelines for gestational diabetes are presented, marking a first in the field. We report on a set of experiments using variants of BERT (BioBERT, DistilBERT, and BERT) and using Large Language Models (LLMs), namely GPT-4 and LLAMA2. Our experiments show that BioBERT performed better than other models, including the Large Language Models, with an average F1-score of 0.72. GPT-4 and LLAMA2 results show similar performance but less consistency. We also release the code and an annotated a corpus of causal statements within the Clinical Practice Guidelines for gestational diabetes.
ISSN:2331-8422