Mining peripheral arterial disease cases from narrative clinical notes using natural language processing.

Journal: Journal of vascular surgery
PMID: 28189359

Abstract

OBJECTIVE: Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative notes and compared the performance of the NLP algorithm with billing code algorithms, using ankle-brachial index test results as the gold standard.

Authors

  • Naveed Afzal
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Sara Abram
    Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minn.
  • Christopher G Scott
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA.
  • Rajeev Chaudhry
    Office of Information and Knowledge Management, Mayo Clinic, Rochester, MN.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Iftikhar J Kullo
    Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minn.
  • Adelaide M Arruda-Olson
    Mayo Clinic Rochester, MN.

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