A Feasibility Study of Literature-Guided HRV Stratification Using Large Language Models

TL;DR

This study explores using large language models to help organize and apply heart rate variability research for clinical decision support, improving transparency and adaptability.

Contribution

A novel LLM-assisted framework for transparent, literature-guided HRV-based risk stratification is introduced.

Findings

The framework achieved 86% accuracy in HRV classification using literature-guided methods.

It outperformed traditional models in transparency and adaptability to new research.

The system demonstrated 81% sensitivity and 87% specificity in HRV-based risk stratification.

Abstract

Background: Heart rate variability (HRV) is a valuable indicator for assessing vascular health, but keeping clinical decision support systems (CDSSs) aligned with the rapidly evolving literature remains challenging. This study aimed to develop an LLM-assisted literature synthesis framework to support transparent HRV-based risk stratification, enabling systematic extraction and organization of HRV evidence from published studies. Methods: An LLM-driven framework was developed to extract HRV parameters from 140 medical abstracts. The system simulated step-by-step human reasoning to identify key HRV indicators and group patient data using predefined statistical thresholds derived from the literature. System performance was evaluated using ECG-derived HRV features as a feasibility evaluation of literature-guided HRV classification. Results: The proposed framework demonstrated an accuracy of 86% in literature-guided HRV classification, with a sensitivity of 81% and a specificity of 87%. Compared with traditional machine learning approaches, the LLM-assisted system provided transparent, literature-grounded reasoning and could be readily updated as new studies became available. Conclusions: Large language models can support evidence-guided parameter selection and feasibility-level HRV-based risk stratification, rather than serving as predictive classifiers. This approach reduces manual effort, enhances transparency, and addresses common “black box” concerns associated with AI-assisted CDSS development in clinical practice.

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