Validation of natural language processing to determine the presence and size of abdominal aortic aneurysms in a large integrated health system.

Journal: Journal of vascular surgery
PMID: 33548429

Abstract

OBJECTIVE: Previous studies of the natural history of abdominal aortic aneurysms (AAAs) have been limited by small cohort sizes or heterogeneous analyses of pooled data. By quickly and efficiently extracting imaging data from the health records, natural language processing (NLP) has the potential to substantially improve how we study and care for patients with AAAs. The aim of the present study was to test the ability of an NLP tool to accurately identify the presence or absence of AAAs and detect the maximal abdominal aortic diameter in a large dataset of imaging study reports.

Authors

  • Myra McLenon
    Softek Illuminate, Inc, Overland Park, Kan.
  • Steven Okuhn
    Division of Vascular Surgery, Department of Surgery, Veterans Affairs San Francisco Healthcare System, San Francisco, Calif; Division of Vascular Surgery, Department of Surgery, University of California, San Francisco, San Francisco, Calif.
  • Elizabeth M Lancaster
    Division of Vascular Surgery, Department of Surgery, University of California, San Francisco, San Francisco, Calif.
  • Michaela M Hull
    Kaiser Permanente Center for Effectiveness and Safety Research, Pasadena, Calif.
  • John L Adams
    Kaiser Permanente Center for Effectiveness and Safety Research, Pasadena, Calif.
  • Elizabeth McGlynn
    Kaiser Permanente Center for Effectiveness and Safety Research, Pasadena, Calif.
  • Andrew L Avins
    Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
  • Robert W Chang
    Division of Research, Kaiser Permanente Northern California, Oakland, Calif; Division of Vascular Surgery, Department of Surgery, The Permanente Medical Group, South San Francisco, Calif. Electronic address: robert.W.Chang@kp.org.

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