Use of Natural Language Processing to Improve Identification of Patients With Peripheral Artery Disease.

Journal: Circulation. Cardiovascular interventions
Published Date: Oct 1, 2020

Abstract

BACKGROUND: Peripheral artery disease (PAD) is underrecognized, undertreated, and understudied: each of these endeavors requires efficient and accurate identification of patients with PAD. Currently, PAD patient identification relies on diagnosis/procedure codes or lists of patients diagnosed or treated by specific providers in specific locations and ways. The goal of this research was to leverage natural language processing to more accurately identify patients with PAD in an electronic health record system compared with a structured data-based approach.

Authors

  • E Hope Weissler
    Division of Vascular and Endovascular Surgery (E.H.W.), Duke University School of Medicine, Durham, NC.
  • Jikai Zhang
    Department of Biostatistics and Bioinformatics (J.Z., R.H.), Duke University School of Medicine, Durham, NC.
  • Steven Lippmann
    Department of Population Health Sciences (S.L., W.S.J.), Duke University School of Medicine, Durham, NC.
  • Shelley Rusincovitch
    Duke Forge (S.R., R.H.), Duke University School of Medicine, Durham, NC.
  • Ricardo Henao
    Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
  • W Schuyler Jones
    Department of Population Health Sciences (S.L., W.S.J.), Duke University School of Medicine, Durham, NC.

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