3 research outputs found
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
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
A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias
Regular blood pressure (BP) monitoring in clinical and ambulatory settings
plays a crucial role in the prevention, diagnosis, treatment, and management of
cardiovascular diseases. Recently, the widespread adoption of ambulatory BP
measurement devices has been driven predominantly by the increased prevalence
of hypertension and its associated risks and clinical conditions. Recent
guidelines advocate for regular BP monitoring as part of regular clinical
visits or even at home. This increased utilization of BP measurement
technologies has brought up significant concerns, regarding the accuracy of
reported BP values across settings.
In this survey, focusing mainly on cuff-based BP monitoring technologies, we
highlight how BP measurements can demonstrate substantial biases and variances
due to factors such as measurement and device errors, demographics, and body
habitus. With these inherent biases, the development of a new generation of
cuff-based BP devices which use artificial-intelligence (AI) has significant
potential. We present future avenues where AI-assisted technologies can
leverage the extensive clinical literature on BP-related studies together with
the large collections of BP records available in electronic health records.
These resources can be combined with machine learning approaches, including
deep learning and Bayesian inference, to remove BP measurement biases and to
provide individualized BP-related cardiovascular risk indexes