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Exploring Gender Disparities in Time to Diagnosis

Applications 2020-11-17 v2 Machine Learning

Abstract

Sex and gender-based healthcare disparities contribute to differences in health outcomes. We focus on time to diagnosis (TTD) by conducting two large-scale, complementary analyses among men and women across 29 phenotypes and 195K patients. We first find that women are consistently more likely to experience a longer TTD than men, even when presenting with the same conditions. We further explore how TTD disparities affect diagnostic performance between genders, both across and persistent to time, by evaluating gender-agnostic disease classifiers across increasing diagnostic information. In both fairness analyses, the diagnostic process favors men over women, contradicting the previous observation that women may demonstrate relevant symptoms earlier than men. These analyses suggest that TTD is an important yet complex aspect when studying gender disparities, and warrants further investigation.

Keywords

Cite

@article{arxiv.2011.06100,
  title  = {Exploring Gender Disparities in Time to Diagnosis},
  author = {Tony Y. Sun and Oliver J. Bear Don't Walk and Jennifer L. Chen and Harry Reyes Nieva and Noémie Elhadad},
  journal= {arXiv preprint arXiv:2011.06100},
  year   = {2020}
}

Comments

Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract

R2 v1 2026-06-23T20:06:47.424Z