Identifying Symptom Information in Clinical Notes Using Natural Language Processing.

Journal: Nursing research
PMID: 33196504

Abstract

BACKGROUND: Symptoms are a core concept of nursing interest. Large-scale secondary data reuse of notes in electronic health records (EHRs) has the potential to increase the quantity and quality of symptom research. However, the symptom language used in clinical notes is complex. A need exists for methods designed specifically to identify and study symptom information from EHR notes.

Authors

  • Theresa A Koleck
    Columbia University, School of Nursing, New York, NY, USA.
  • Nicholas P Tatonetti
    Departments of Biomedical Informatics, Systems Biology, and Medicine, Columbia University, 622 West 168th St VC5, New York, NY 10032, USA. Electronic address: nick.tatonetti@columbia.edu.
  • Suzanne Bakken
    Columbia University, School of Nursing, New York, NY, USA; Columbia University, Department of Biomedical Informatics, New York, NY, USA; Columbia University, Data Science Institute, New York, NY, USA. Electronic address: sbh22@cumc.columbia.edu.
  • Shazia Mitha
  • Morgan M Henderson
  • Maureen George
  • Christine Miaskowski
    University of California, San Francisco, United States of America.
  • Arlene Smaldone
    Columbia University School of Nursing, Columbia University College of Dental Medicine, New York, NY.
  • Maxim Topaz
    Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.

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