Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records.

Journal: Journal of pain and symptom management
PMID: 29496537

Abstract

CONTEXT: Clinicians document cancer patients' symptoms in free-text format within electronic health record visit notes. Although symptoms are critically important to quality of life and often herald clinical status changes, computational methods to assess the trajectory of symptoms over time are woefully underdeveloped.

Authors

  • Alexander W Forsyth
    Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts.
  • Regina Barzilay
    Computer Science and Artificial Intelligence Laboratory , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , MA 02139 , USA . Email: regina@csail.mit.edu.
  • Kevin S Hughes
    Division of Surgical Oncology, MGH, Boston, USA.
  • Dickson Lui
    Department of Medicine, Waitemata District Health Board, Auckland, New Zealand.
  • Karl A Lorenz
    Department of Medicine, Primary Care and Population Health, Stanford School of Medicine, Stanford, California; VA Palo Alto Health Care System, Palo Alto, California.
  • Andrea Enzinger
    Division of Population Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts; Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts; Division of Palliative Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
  • James A Tulsky
    Harvard Medical School, Boston, MA.
  • Charlotta Lindvall
    Harvard Medical School, Boston, MA.

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