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Related Experiment Video

Updated: Jul 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large Language Models to Extract Cancer Staging Data From Clinical Documentation at Scale.

Swapna Abhyankar1, Rajesh M Rao1, Mehraveh Salehi1

  • 1Truveta, Inc, Bellevue, WA.

JCO Clinical Cancer Informatics
|July 13, 2026
PubMed
Summary

A new large language model (LLM), TLM-Oncology, precisely extracts oncology staging data from clinical notes for multiple cancer types. This advances the use of previously inaccessible real-world data for cancer research.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Natural Language Processing

Background:

  • Extracting accurate cancer staging data from clinical documentation is crucial for patient care and research.
  • Manual extraction is time-consuming and prone to errors, limiting the use of real-world data.

Purpose of the Study:

  • To develop and evaluate the Truveta Language Model Oncology (TLM-Oncology), a large language model (LLM), for precise extraction of real-world oncology staging data.
  • To assess the model's performance across multiple cancer types using clinical documentation.

Main Methods:

  • A pretrained LLM was fine-tuned using supervised learning on annotated clinical notes for bladder, cervical, colorectal, breast, and prostate cancers.
  • Performance was evaluated using precision, recall, and F1 scores at both relation and attribute levels.

Related Experiment Videos

Last Updated: Jul 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Main Results:

  • TLM-Oncology extracted over 2.5 million staging records for 217,768 patients from over two million notes.
  • High relation-level precision (0.77-1.0) was achieved across multiple cancer types, demonstrating the model's effectiveness.

Conclusions:

  • TLM-Oncology successfully extracts detailed cancer staging information from diverse clinical documentation with high precision.
  • The model transforms previously inaccessible data into a valuable resource for downstream applications in oncology research and care.