Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes.

Journal: JCO clinical cancer informatics
Published Date: May 1, 2023

Abstract

PURPOSE: There is an unmet need to empirically explore and understand drivers of cancer disparities, particularly social determinants of health. We explored natural language processing methods to automatically and empirically extract clinical documentation of social contexts and needs that may underlie disparities.

Authors

  • Abigail Derton
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
  • Marco Guevara
    Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA.
  • Shan Chen
    National Academy of Economic Security, Beijing Jiaotong University, Beijing 100044, China.
  • Shalini Moningi
    Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA.
  • David E Kozono
    Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.
  • Dianbo Liu
    Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge.
  • Timothy A Miller
    Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, United States.
  • Guergana K Savova
    Department of Pediatrics, Children's Hospital of Boston, Boston.
  • Raymond H Mak
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Danielle S Bitterman
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.

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