Leveraging Few-Shot Learning and Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
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Computer Science > Computation and Language
arXiv:2402.01826 (cs)
[Submitted on 2 Feb 2024 (v1), last revised 14 Aug 2026 (this version, v2)]
Title:Leveraging Few-Shot Learning and Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
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Abstract:Current blood pressure (BP) technologies and standards were established decades ago, and these standards are still used worldwide today, often without adjusting BP readings for individual demographic factors such as sex and age. While these standards provide useful guidelines and help identify at-risk patients, they are not fully reliable for diagnosis due to the lack of demographic considerations. This study aims to assess the feasibility of using large language models (LLMs) for the automated extraction of BP-related information from the scientific literature, with a focus on biological sex-based distinctions in BP distributions.
We employed natural language processing (NLP) methods to extract the means and standard deviations of BP values from the literature, distinguishing by biological sex. We developed a Solr-based search engine to retrieve scientific articles containing BP-related keywords and biological sex indicators from PubMed. From the retrieved articles, we created a manually reviewed subset comprising 213 articles including 90 cases that reported BP values based on biological sex. We experimented with one few-shot learning method and two zero-shot LLM-based methods---LLaMA3 and GPT-3.5---to extract the mean and standard deviations of BP values, and the associated biological sex. Based on the automatically-extracted information, we generated heatmaps and contour plots to study the variations of BP values across biological sex.
| Comments: | Accepted by the journal of Computers in Biology and Medicine |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2402.01826 [cs.CL] |
| (or arXiv:2402.01826v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2402.01826
arXiv-issued DOI via DataCite
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| Related DOI: | https://doi.org/10.1016/j.compbiomed.2025.111128
DOI(s) linking to related resources
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Submission history
From: Yuting Guo [view email][v1] Fri, 2 Feb 2024 18:15:51 UTC (291 KB)
[v2] Fri, 14 Aug 2026 13:31:50 UTC (314 KB)
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