External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-reported Overactive Bladder Treatment Outcomes.

Journal: Urology
PMID: 39242047

Abstract

OBJECTIVE: To predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesical botulinum toxin (OBTX-A) injection. We sought to validate the models in a challenging setting using an external dataset of a markedly different patient cohort and dosing regimen. We hypothesized the model would outperform human experts and top available algorithms.

Authors

  • Glenn T Werneburg
    Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA; Department of Urology, University of Michigan, Ann Arbor, MI, USA. Electronic address: wernebg@ccf.org.
  • Eric A Werneburg
    Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY.
  • Howard B Goldman
  • Emily Slopnick
    Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH.
  • Ly Hoang Roberts
    Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH.
  • Sandip P Vasavada
    Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA. Electronic address: vasavas@ccf.org.

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