Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 39545362

Abstract

OBJECTIVES: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups.

Authors

  • Jessica Sperling
    Duke University Social Science Research Institute, Durham, NC.
  • Whitney Welsh
    Social Science Research Institute, Duke University, Durham, NC 27708, United States.
  • Erin Haseley
    Social Science Research Institute, Duke University, Durham, NC 27708, United States.
  • Stella Quenstedt
    Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC 27701, United States.
  • Perusi B Muhigaba
    Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC 27701, United States.
  • Adrian Brown
    Social Science Research Institute, Duke University, Durham, NC 27708, United States.
  • Patti Ephraim
    Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States.
  • Tariq Shafi
    Division of Nephrology, The University of Mississippi Medical Center, Jackson, MS, USA.
  • Michael Waitzkin
    Science & Society, Duke University, Durham, NC 27708, United States.
  • David Casarett
    Department of Medicine/Division of General Internal Medicine/Palliative Care, Duke University, Durham, North Carolina, USA.
  • Benjamin A Goldstein
    Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina.

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