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Biomarkers of erosive arthritis in systemic lupus erythematosus: Application of machine learning models.

Fulvia CeccarelliMarco SciandroneCarlo PerriconeGiulio GalvanEnrica CiprianoAlessandro GalligariTommaso LevatoTania ColasantiLaura MassaroFrancesco NatalucciFrancesca Romana SpinelliCristiano AlessandriGuido ValesiniFabrizio Conti
Published in: PloS one (2018)
The application of Machine Learning Models allowed to identify factors associated with US-detected erosive bone damage in a large SLE cohort and their relevance in determining this phenotype. Although the scope of this study is limited by the small sample size and its cross-sectional nature, the results suggest the relevance of ACPA and anti-CarP antibodies in the development of erosive damage as also pointed out in other studies.
Keyphrases
  • machine learning
  • cross sectional
  • oxidative stress
  • artificial intelligence
  • systemic lupus erythematosus
  • rheumatoid arthritis
  • big data
  • deep learning
  • disease activity
  • soft tissue
  • body composition
  • bone regeneration