Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes.
May Yee ChoiIrene ChenAnn Elaine ClarkeMarvin J FritzlerKatherine A BuhlerMurray B UrowitzJohn HanlyYvan St-PierreCaroline GordonSang-Cheol BaeJuanita Romero-DiazJorge Sanchez-GuerreroSasha BernatskyDaniel J WallaceDavid Alan IsenbergAnisur RahmanJoan T MerrillPaul R FortinDafna D GladmanIan N BruceMichelle A PetriEllen M GinzlerMary Anne DooleyRosalind Ramsey-GoldmanSusan ManziAndreas JönsenGraciela S AlarconRonald F Van VollenhovenCynthia AranowMeggan MackayGuillermo Ruiz-IrastorzaSam LimMurat InançKenneth KalunianSøren JacobsenChristine PeschkenDiane L KamenAnca AskanaseJill P BuyonDavid SontagKaren H CostenbaderPublished in: Annals of the rheumatic diseases (2023)
Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.
Keyphrases
- disease activity
- systemic lupus erythematosus
- rheumatoid arthritis
- machine learning
- rheumatoid arthritis patients
- ankylosing spondylitis
- juvenile idiopathic arthritis
- cross sectional
- genome wide
- case report
- big data
- dna methylation
- artificial intelligence
- type diabetes
- combination therapy
- metabolic syndrome
- replacement therapy
- insulin resistance