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Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.

Charat ThongprayoonPradeep VaitlaCaroline C JadlowiecNapat LeeaphornShennen A MaoMichael A MaoFahad QureshiWisit KaewputFawad QureshiSupawit TangpanithandeePajaree KrisanapanPattharawin PattharanitimaPrakrati C AcharyaPitchaphon NissaisorakarnMatthew CooperWisit Cheungpasitporn
Published in: Medicines (Basel, Switzerland) (2023)
= 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.
Keyphrases
  • machine learning
  • single cell
  • artificial intelligence
  • healthcare
  • rna seq
  • big data
  • palliative care
  • case report
  • deep learning
  • clinical practice
  • adipose tissue
  • chronic pain
  • free survival