Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance - Takeaways - MDSpire

Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance

  • By

  • Ingrid Luo

  • Anna Graber-Naidich

  • Mengrui Zhang

  • Rakshit Kaushik

  • Grant M. Nieda

  • Tony Chen

  • Bo Gu

  • Eunji Choi

  • Victoria Y. Ding

  • Fatma Gunturkun

  • Mina Satoyoshi

  • Archana Bhat

  • Tae Yoon Lee

  • Chloe C. Su

  • Timothy John Ellis-Caleo

  • A. Solomon Henry

  • Manisha Desai

  • Leah M. Backhus

  • Natalie S. Lui

  • Ann Leung

  • Joel W. Neal

  • Allison W. Kurian

  • Curtis P. Langlotz

  • Heather A. Wakelee

  • Su-Ying Liang

  • Aparajita Khan

  • Summer S. Han

  • November 28, 2025

  • 0 min

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  • 1

    Accurate smoking documentation in EHRs is essential for risk assessment and monitoring in lung cancer patients.

  • 2

    Generative LLMs demonstrated over 96% accuracy in extracting smoking history from clinical notes, outperforming BERT-based models.

  • 3

    The developed framework combines LLMs with rule-based techniques to enhance the quality of longitudinal smoking data.

  • 4

    Deployment of the LLM framework on 79,408 notes improved lung cancer surveillance compared to NCCN Guidelines.

  • 5

    This study highlights the potential of LLMs to improve smoking history documentation, benefiting broader clinical applications.

Original Source(s)

Leveraging large language models to extract smoking history from clinical notes for lung cancer surveillance

  • by Ingrid Luo, Anna Graber-Naidich, Mengrui Zhang, Rakshit Kaushik, Grant M. Nieda, Tony Chen, Bo Gu, Eunji Choi, Victoria Y. Ding, Fatma Gunturkun, Mina Satoyoshi, Archana Bhat, Tae Yoon Lee, Chloe C. Su, Timothy John Ellis-Caleo, A. Solomon Henry, Manisha Desai, Leah M. Backhus, Natalie S. Lui, Ann Leung, Joel W. Neal, Allison W. Kurian, Curtis P. Langlotz, Heather A. Wakelee, Su-Ying Liang, Aparajita Khan, Summer S. Han

  • November 28, 2025

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