Empowering large language models for automated clinical assessment with generation-augmented retrieval and hierarchical chain-of-thought
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces GAPrompt, a novel method using large language models (LLMs) to automatically analyze electronic health records (EHRs) for quantitative clinical assessments, like stroke severity. GAPrompt enhances LLM performance in healthcare by improving data relevance and accuracy.
Area Of Science
- Artificial Intelligence in Medicine
- Natural Language Processing for Healthcare
- Clinical Data Analysis
Background
- Electronic health records (EHRs) contain valuable data for healthcare improvement.
- Large language models (LLMs) show potential for automating EHR analysis.
- Current LLMs often lack real-world healthcare relevance for clinical applications.
Purpose Of The Study
- To develop a novel prompting paradigm, GAPrompt, to enhance generic LLMs for automated clinical assessment.
- To specifically address quantitative stroke severity assessment using EHR data.
- To overcome limitations of current LLMs in processing complex clinical information.
Main Methods
- GAPrompt utilizes prompt-driven LLM selection and knowledge base construction.
- Employs summary-based generation-augmented retrieval (SGAR) for enhanced data access.
- Incorporates hierarchical chain-of-thought (HCoT) inferencing and ensembling for robustness.
Main Results
- GAPrompt progressively addresses LLM limitations in clinical settings.
- Demonstrates improved LLM applicability, knowledge understanding, and inference precision.
- Experimental results confirm LLMs' capability to automatically assess EHRs and generate quantitative clinical results.
Conclusions
- GAPrompt enhances foundation LLMs for domain-specific tasks like automated EHR analysis.
- Addresses challenges in labor-intensive quantitative stroke assessment.
- Offers a practical paradigm for leveraging LLMs in medicine and other fields.
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