Using an artificial intelligence tool incorporating natural language processing to identify patients with a diagnosis of ANCA-associated vasculitis in electronic health records.

Journal: Computers in biology and medicine
PMID: 38039893

Abstract

BACKGROUND: Because anti-neutrophil cytoplasmatic antibody (ANCA)-associated vasculitis (AAV) is a rare, life-threatening, auto-immune disease, conducting research is difficult but essential. A long-lasting challenge is to identify rare AAV patients within the electronic-health-record (EHR)-system to facilitate real-world research. Artificial intelligence (AI)-search tools using natural language processing (NLP) for text-mining are increasingly postulated as a solution.

Authors

  • Jolijn R van Leeuwen
    Center of Expertise for Lupus-, Vasculitis- and Complement-mediated Systemic diseases (LuVaCs), Department of Internal Medicine - Nephrology Section, Leiden University Medical Center, Leiden, the Netherlands.
  • Erik L Penne
    Department of Internal Medicine - Nephrology Section, Northwest Clinics, Alkmaar, the Netherlands.
  • Ton Rabelink
    Center of Expertise for Lupus-, Vasculitis- and Complement-mediated Systemic diseases (LuVaCs), Department of Internal Medicine - Nephrology Section, Leiden University Medical Center, Leiden, the Netherlands.
  • Rachel Knevel
    Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands r.knevel@lumc.nl.
  • Y K Onno Teng
    Center of Expertise for Lupus-, Vasculitis- and Complement-mediated Systemic diseases (LuVaCs), Department of Internal Medicine - Nephrology Section, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: Y.K.O.Teng@lumc.nl.

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