Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations.

Journal: Lupus
PMID: 35835534

Abstract

OBJECTIVES: Systemic lupus erythematosus (SLE) is a heterogeneous disease characterized by disease flares which can require hospitalization. Our objective was to apply machine learning methods to predict hospitalizations for SLE from electronic health record (EHR) data.

Authors

  • April M Jorge
    Division of Rheumatology, Allergy, and Immunology, Harvard Medical School, 2348Massachusetts General Hospital, Boston, MA, USA.
  • Dylan Smith
    Department of Computer and Information Sciences, 5923Fordham University, New York, NY, USA.
  • Zhiyao Wu
    Department of Computer and Information Sciences, 5923Fordham University, New York, NY, USA.
  • Tashrif Chowdhury
    Department of Computer and Information Sciences, 5923Fordham University, New York, NY, USA.
  • Karen Costenbader
    Division of Rheumatology, Inflammation, and Immunity, Harvard Medical School, 1861Brigham and Women's Hospital, Boston, MA, USA.
  • Yuqing Zhang
    Division of Rheumatology, Allergy, and Immunology, Harvard Medical School, 2348Massachusetts General Hospital, Boston, MA, USA.
  • Hyon K Choi
    Division of Rheumatology, Allergy, and Immunology, Harvard Medical School, 2348Massachusetts General Hospital, Boston, MA, USA.
  • Candace H Feldman
    Division of Rheumatology, Inflammation, and Immunity, Harvard Medical School, 1861Brigham and Women's Hospital, Boston, MA, USA.
  • Yijun Zhao
    Department of Computer Science, Tufts University, Medford, Massachusetts, United States of America.

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