Automated HEART score determination via ChatGPT: Honing a framework for iterative prompt development
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a framework for improving large language model (LLM) accuracy in extracting clinical data. Prompt refinement enhanced automated HEART score determination, showing potential for clinical note analysis.
Area Of Science
- Artificial Intelligence in Medicine
- Clinical Informatics
- Natural Language Processing
Background
- Large language models (LLMs) show promise for analyzing clinical notes.
- Accurate extraction of clinical data is crucial for patient care.
- Automating risk score calculation can streamline clinical workflows.
Purpose Of The Study
- To present a design framework for enhancing LLM accuracy in clinical note analysis.
- To demonstrate the framework's utility via prompt refinement for automated HEART score determination.
- To evaluate the impact of iterative prompt design on LLM performance in extracting complex clinical information.
Main Methods
- Developed a pipeline for LLM prompt testing using stochastic repeat testing.
- Quantified response errors against physician assessments.
- Iteratively refined prompts for HEART score subcomponents across multiple LLM query rounds.
Main Results
- Iterative prompt design reduced erroneous non-numerical responses for HEART subscores.
- GPT-4 showed improved accuracy in numerical HEART subscore responses, with mean error decreasing from 0.16 to 0.10.
- The framework facilitated enhanced LLM performance in extracting and applying clinical data.
Conclusions
- Established a framework for iterative prompt design in clinical applications of LLMs.
- Demonstrated LLMs' potential for structured clinical note analysis.
- Highlighted the need for validation on large-scale, real-world clinical data with privacy safeguards.
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