Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

Journal: Diabetes care
Published Date: Aug 1, 2020

Abstract

OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH).

Authors

  • Anita D Misra-Hebert
    Department of Internal Medicine and Center for Value-Based Care Research, Cleveland Clinic Community Care, Cleveland Clinic, Cleveland, OH misraa@ccf.org.
  • Alex Milinovich
    Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
  • Alex Zajichek
    Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
  • Xinge Ji
    Department of Quantitative Health Sciences, Lerner Research Institute (X.J., J.B.), Cleveland Clinic, OH.
  • Todd D Hobbs
    Novo Nordisk, Inc., Plainsboro, NJ.
  • Wayne Weng
    Novo Nordisk, Inc., Plainsboro, NJ.
  • Paul Petraro
    Novo Nordisk, Inc., Plainsboro, NJ.
  • Sheldon X Kong
    Novo Nordisk, Inc., Plainsboro, NJ.
  • Michelle Mocarski
    Novo Nordisk, Inc., Plainsboro, NJ.
  • Rahul Ganguly
    Novo Nordisk, Inc., Plainsboro, NJ.
  • Janine M Bauman
    Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH.
  • Kevin M Pantalone
    Department of Endocrinology, Endocrinology & Metabolism Institute, Cleveland Clinic, Cleveland, OH.
  • Robert S Zimmerman
    Department of Endocrinology, Endocrinology & Metabolism Institute, Cleveland Clinic, Cleveland, OH.
  • Michael W Kattan

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