Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

Journal: International journal of medical informatics
PMID: 39864108

Abstract

BACKGROUND: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.

Authors

  • Liqin Wang
    Brigham and Women's Hospital, Boston, MA, USA.
  • John Novoa-Laurentiev
    Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School Boston MA USA. Electronic address: jlaurentiev@bwh.harvard.edu.
  • Claire Cook
    Massachusetts General Hospital and Harvard Medical School, Boston.
  • Shruthi Srivatsan
    Rheumatology and Allergy Clinical Epidemiology Research Center and Division of Rheumatology, Allergy, and Immunology, and Mongan Institute, Department of Medicine, Massachusetts General Hospital Boston MA USA. Electronic address: ssrivatsan1@mgh.harvard.edu.
  • Yining Hua
    Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Eli Miloslavsky
    Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital and Harvard Medical School Boston MA USA. Electronic address: emiloslavsky@mgh.harvard.edu.
  • Hyon K Choi
    Division of Rheumatology, Allergy, and Immunology, Harvard Medical School, 2348Massachusetts General Hospital, Boston, MA, USA.
  • Li Zhou
    School of Education, China West Normal University, Nanchong, Sichuan, China.
  • Zachary S Wallace
    Massachusetts General Hospital and Harvard Medical School, Boston.

Keywords

External Resources

Popular Topics
Recent Journals