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Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and

Abhishek Shivanna1, Austin Fitts2, Jordan Tschida1

  • 1Advanced Computing for Health Sciences, Oak Ridge National Laboratory, Oak Ridge, TN.

Journal of Registry Management
|May 11, 2026
PubMed
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This summary is machine-generated.

Large language models (LLMs) can automate oncology treatment extraction from clinical text. The 8B-parameter Llama model offers a balance of accuracy and efficiency for cancer registries.

Area of Science:

  • Oncology
  • Natural Language Processing
  • Health Informatics

Background:

  • Collecting comprehensive cancer treatment data from medical records is crucial for real-world evidence studies.
  • Unstructured clinical text hinders systematic, high-quality treatment information extraction.
  • Manual data extraction is time-consuming, necessitating automated solutions.

Purpose of the Study:

  • To evaluate the utility of Llama family large language models (LLMs) for automated oncology treatment information extraction.
  • To guide researchers in utilizing cancer registry data for insights beyond clinical trials.

Main Methods:

  • Four instruction-tuned Llama models (1B, 3B, 8B, 70B parameters) were assessed for treatment extraction from clinical documents.
  • A unified oncology knowledge base was developed for standardizing extracted entities.
Keywords:
Artificial intelligencecancer registrydata extractiondata normalizationlarge language modelsnatural language processingoncology informatics

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  • Performance was measured using accuracy metrics (Precision, Recall, F1-Score) and operational feasibility.

    • Main Results:

    • A positive correlation between model size and extraction accuracy was observed, with F1-scores ranging from 0.609 (1B) to 0.828 (70B).
    • Larger models showed higher accuracy and compliance but incurred greater computational costs.
    • The 8B model demonstrated strong performance, with diminishing returns noted for the 70B model.

    Conclusions:

    • LLMs are a viable technology for automating oncology treatment extraction.
    • The 8B-parameter LLM provides an effective balance of accuracy and computational efficiency.
    • Standardized data integration via a knowledge base enhances real-world evidence analyses.