Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) enable accurate data extraction from pathology reports for kidney tumors. This novel pipeline efficiently structures complex clinical data, improving cancer research.
Area Of Science
- Computational pathology
- Artificial intelligence in medicine
- Natural language processing
Background
- Extracting structured data from unstructured clinical text is challenging.
- Traditional methods face limitations in complex medical domains.
- Pathology reports contain vital information for cancer diagnosis and research.
Purpose Of The Study
- To develop and validate a novel pipeline for accurate information extraction and normalization from unstructured pathology reports using large language models (LLMs).
- To focus initially on kidney tumor reports and demonstrate adaptability to other cancer types.
- To generate analysis-ready tabular data from free-text medical records at scale.
Main Methods
- An end-to-end pipeline leveraging LLMs for information extraction and normalization.
- Flexible prompt templates and direct production of tabular data.
- A human-in-the-loop iterative refinement process guided by an error ontology.
- Validation on 2,297 kidney tumor reports and publicly available breast and prostate cancer reports.
Main Results
- Achieved a macro-averaged F1 score of 0.99 for kidney tumor subtypes and 0.97 for kidney metastasis detection.
- Demonstrated flexibility with multiple LLM backbones.
- Showcased adaptability to new domains (breast and prostate cancer reports).
Conclusions
- LLM-based pipelines offer a highly accurate and efficient solution for extracting structured data from complex clinical text.
- The developed pipeline successfully structures critical information from pathology reports, facilitating analysis and research.
- Emphasizes the importance of task definition, interdisciplinary collaboration, and complexity management in clinical LLM workflows.
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