A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.

Journal: Osteoarthritis and cartilage
PMID: 31002938

Abstract

OBJECTIVE: Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progression phenotypes that are potentially more responsive to interventions.

Authors

  • A E Nelson
    Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: aenelson@med.unc.edu.
  • F Fang
    Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: fangf@live.unc.edu.
  • L Arbeeva
    Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: liubov_arbeeva@med.unc.edu.
  • R J Cleveland
    Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: Rebecca_cleveland@med.unc.edu.
  • T A Schwartz
    Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: tschwart@email.unc.edu.
  • L F Callahan
    Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: leigh_callahan@med.unc.edu.
  • J S Marron
    Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: marron@unc.edu.
  • R F Loeser
    Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: Richard_loeser@med.unc.edu.

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