Identifying Bladder Phenotypes After Spinal Cord Injury With Unsupervised Machine Learning: A New Way to Examine Urinary Symptoms and Quality of Life.

Journal: The Journal of urology
PMID: 38626440

Abstract

PURPOSE: Patients with spinal cord injuries (SCIs) experience variable urinary symptoms and quality of life (QOL). Our objective was to use machine learning to identify bladder-relevant phenotypes after SCI and assess their association with urinary symptoms and QOL.

Authors

  • Blayne Welk
    Department of Surgery, Western University, London, Ontario, Canada.
  • Tianyue Zhong
    Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada.
  • Jeremy Myers
    Department of Surgery (Urology), University of Utah, Salt Lake City, Utah.
  • John Stoffel
    Department of Urology, University of Michigan, Ann Arbor, Michigan.
  • Sean Elliot
    Department of Urology, University of Minnesota, Minneapolis, Minnesota.
  • Sara M Lenherr
    Department of Surgery (Urology), University of Utah, Salt Lake City, Utah.
  • Daniel Lizotte
    Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada.

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