Natural language processing for prediction of readmission in posterior lumbar fusion patients: which free-text notes have the most utility?

Journal: The spine journal : official journal of the North American Spine Society
Published Date: Aug 15, 2021

Abstract

BACKGROUND CONTEXT: The increasing volume of free-text notes available in electronic health records has created an opportunity for natural language processing (NLP) algorithms to mine this unstructured data in order to detect and predict adverse outcomes. Given the volume and diversity of documentation available in spine surgery, it remains unclear which types of documentation offer the greatest value for prediction of adverse outcomes.

Authors

  • Aditya V Karhade
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Ophelie Lavoie-Gagne
    Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Nicole Agaronnik
    Health Policy Research Center-Mongan Institute, Massachusetts General Hospital, Boston.
  • Hamid Ghaednia
    Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Austin K Collins
    Orthopedic Oncology Service, Massachusetts General Hospital, Harvard Medical School, Boston, USA;
  • David Shin
    School of Medicine, Loma Linda University, Loma Linda, CA, USA.
  • Joseph H Schwab
    Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. Electronic address: jhschwab@mgh.harvard.edu.

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