  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.
