LLMs used to analyze blood pressure variations by sex from literature

Researchers have explored the use of Large Language Models (LLMs) to analyze blood pressure variations across biological sexes from scientific literature. The study utilized NLP techniques and an Apache Solr-based search engine to retrieve relevant articles from PubMed. Experiments with few-shot learning and zero-shot LLMs like Llama 3 and GPT-3.5 were conducted to extract mean and standard deviation of blood pressure values, along with associated biological sex indicators. AI

IMPACT This research demonstrates LLMs' potential in extracting nuanced biological data from scientific literature, potentially improving health research.

RANK_REASON The item is an academic paper detailing research methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• Apache Solr
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• GPT-3.5
• Hugging Face
• Llama 3
• PubMed
• ScienceCast
• Yuting Guo

• paper
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