Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.

Journal: JMIR cancer
PMID: 40153782

Abstract

BACKGROUND: Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records, which is useful for accelerating cancer clinical research and informing oncology care.

Authors

  • David Chen
    Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
  • Saif Addeen Alnassar
    Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
  • Kate Elizabeth Avison
    Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
  • Ryan S Huang
    Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
  • Srinivas Raman
    Department of Radiation Oncology, BC Cancer Vancouver, 600 W 10th Ave, Vancouver, BC, V5Z 4E6, Canada, 1 416-946-4501.

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