Summary of Leveraging Large Language Models For Analyzing Blood Pressure Variations Across Biological Sex From Scientific Literature, by Yuting Guo et al.

Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature

by Yuting Guo, Seyedeh Somayyeh Mousavi, Reza Sameni, Abeed Sarker

First submitted to arxiv on: 2 Feb 2024

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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 paper presents a novel approach to studying blood pressure (BP) measurements using large language models (LLMs). The authors used GPT-35-turbo, an LLM, to extract mean and standard deviation values of BP from a dataset of 25 million abstracts sourced from PubMed. They analyzed the variability of BP across biological sex, finding that this method can be viable for studying BP variations across different demographic factors.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Hypertension is a major public health concern, linked to increased mortality rates worldwide. However, current BP measurement methods and standards may be biased and inconclusive due to lack of consideration for clinical outcomes, comorbidities, or demographic factors. This study uses artificial intelligence (AI) to automatically extract BP data from PubMed abstracts, exploring variations across biological sex. The results show that AI models like GPT-35-turbo can help analyze BP trends and provide valuable insights.

Keywords

» Artificial intelligence » Gpt

Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature

by Yuting Guo, Seyedeh Somayyeh Mousavi, Reza Sameni, Abeed Sarker

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GrooveSquid.com Paper Summaries

Keywords

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