A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

Journal: Rheumatology (Oxford, England)
PMID: 38895877

Abstract

OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout.

Authors

  • Shay Brikman
    Rheumatic Diseases Unit, Emek Medical Center, Afula, Israel.
  • Liel Serfaty
    Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
  • Ran Abuhasira
    Clinical Research Center, Soroka University Medical Center, Be'er Sheva, Israel.
  • Naomi Schlesinger
    Division of Rheumatology, Department of Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.
  • Amir Bieber
    Rheumatic Diseases Unit, Emek Medical Center, Afula, Israel.
  • Nadav Rappoport
    Department of Software and Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.

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