Fairness in Predicting Cancer Mortality Across Racial Subgroups.

Journal: JAMA network open
PMID: 38985468

Abstract

IMPORTANCE: Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so that existing racial disparities are not exacerbated.

Authors

  • Teja Ganta
    Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Arash Kia
    Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.
  • Prathamesh Parchure
    Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Min-Heng Wang
    Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Melanie Besculides
    Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Madhu Mazumdar
    Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
  • Cardinale B Smith
    Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, New York.

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