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Related Experiment Video

Updated: Feb 28, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A Feasibility Study of Literature-Guided HRV Stratification Using Large Language Models.

Tien-Yu Hsu1,2, Gau-Jun Tang3, Cheng-Han Wu2,4

  • 1Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

Large language models can aid in heart rate variability (HRV) risk stratification by synthesizing research. This LLM-assisted framework improves transparency and reduces manual effort in clinical decision support systems.

Keywords:
cardiovascularcerebrovascularclinical decision support systemheart rate variabilitylarge language modelsliterature mining

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Heart rate variability (HRV) is crucial for vascular health assessment.
  • Clinical decision support systems (CDSSs) struggle to keep pace with evolving HRV literature.
  • Systematic literature synthesis is needed for accurate HRV-based risk stratification.

Purpose of the Study:

  • To develop an LLM-assisted framework for synthesizing HRV literature.
  • To support transparent risk stratification using HRV evidence.
  • To enable systematic extraction and organization of HRV data from studies.

Main Methods:

  • An LLM-driven framework extracted HRV parameters from 140 medical abstracts.
  • The system simulated human reasoning for identifying HRV indicators and grouping patient data.
  • Performance was evaluated using ECG-derived HRV features for literature-guided classification.

Main Results:

  • The framework achieved 86% accuracy, 81% sensitivity, and 87% specificity in HRV classification.
  • The LLM-assisted system offered transparent, literature-grounded reasoning.
  • It demonstrated adaptability to new research, unlike traditional machine learning.

Conclusions:

  • LLMs can support evidence-guided parameter selection for HRV risk stratification.
  • This approach enhances transparency and addresses 'black box' concerns in AI-assisted CDSS.
  • LLMs reduce manual effort in clinical decision support development.