Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

Journal: Health care management science
PMID: 39495385

Abstract

BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in healthcare in America. For cancer specifically, one of the leading observed disparities is worse mortality among non-Hispanic Black patients compared to non-Hispanic White patients across the cancer care continuum. These real-world disparities are reflected in the data used to inform the decisions made to alleviate such inequities. Failing to account for inherently biased data underlying these observations could intensify racial cancer disparities and lead to misguided efforts that fail to appropriately address the real causes of health inequity.

Authors

  • Cameron Trentz
    Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.
  • Jacklyn Engelbart
    Epidemiology Department, University of Iowa, Iowa City, Iowa, USA.
  • Jason Semprini
    Health Management & Policy Department, University of Iowa, Iowa City, Iowa, USA.
  • Amanda Kahl
    Epidemiology Department, University of Iowa, Iowa City, Iowa, USA.
  • Eric Anyimadu
    Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.
  • John Buatti
    Department of Radiation Oncology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
  • Thomas Casavant
    Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, USA.
  • Mary Charlton
    Epidemiology Department, University of Iowa, Iowa City, Iowa, USA.
  • Guadalupe Canahuate
    The University of Iowa Iowa City, Iowa, USA.

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