Does synthetic data augmentation improve the performances of machine learning classifiers for identifying health problems in patient-nurse verbal communications in home healthcare settings?

Journal: Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PMID: 38961517

Abstract

BACKGROUND: Identifying health problems in audio-recorded patient-nurse communication is important to improve outcomes in home healthcare patients who have complex conditions with increased risks of hospital utilization. Training machine learning classifiers for identifying problems requires resource-intensive human annotation.

Authors

  • Jihye Kim Scroggins
    Columbia University School of Nursing, New York, New York, USA.
  • Maxim Topaz
    Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Jiyoun Song
    School of Nursing, Columbia University, New York, New York, USA.
  • Maryam Zolnoori
    Columbia University School of Nursing, New York, NY.

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