Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
This work addresses the lack of data-driven research on blood pressure variations across demographic factors, which is important for improving diagnostic standards in public health, though it is incremental in applying existing LLM methods to a new dataset.
The study used GPT-35-turbo to extract blood pressure data from PubMed abstracts, analyzing variations across biological sex, and demonstrated the viability of LLMs for such demographic analyses.
Hypertension, defined as blood pressure (BP) that is above normal, holds paramount significance in the realm of public health, as it serves as a critical precursor to various cardiovascular diseases (CVDs) and significantly contributes to elevated mortality rates worldwide. However, many existing BP measurement technologies and standards might be biased because they do not consider clinical outcomes, comorbidities, or demographic factors, making them inconclusive for diagnostic purposes. There is limited data-driven research focused on studying the variance in BP measurements across these variables. In this work, we employed GPT-35-turbo, a large language model (LLM), to automatically extract the mean and standard deviation values of BP for both males and females from a dataset comprising 25 million abstracts sourced from PubMed. 993 article abstracts met our predefined inclusion criteria (i.e., presence of references to blood pressure, units of blood pressure such as mmHg, and mention of biological sex). Based on the automatically-extracted information from these articles, we conducted an analysis of the variations of BP values across biological sex. Our results showed the viability of utilizing LLMs to study the BP variations across different demographic factors.