Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records.

Journal: JCO clinical cancer informatics
Published Date: Apr 1, 2021

Abstract

PURPOSE: Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.

Authors

  • Jiaming Zeng
    Department of Management Science and Engineering, Huang Engineering Center, Stanford, CA.
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • A Solomon Henry
    Research Informatics Center, Stanford University, Stanford, CA.
  • Douglas J Wood
    Research Informatics Center, Stanford University, Stanford, CA.
  • Ross D Shachter
    Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA.
  • Michael F Gensheimer
    Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.

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