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Review
. 2022 Aug 12;3(8):100534.
doi: 10.1016/j.patter.2022.100534.

Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records

Kendra Albert  1 , Maggie Delano  2
Affiliations
Review

Sex trouble: Sex/gender slippage, sex confusion, and sex obsession in machine learning using electronic health records

Kendra Albert et al. Patterns (N Y). .

Abstract

False assumptions that sex and gender are binary, static, and concordant are deeply embedded in the medical system. As machine learning researchers use medical data to build tools to solve novel problems, understanding how existing systems represent sex/gender incorrectly is necessary to avoid perpetuating harm. In this perspective, we identify and discuss three factors to consider when working with sex/gender in research: "sex/gender slippage," the frequent substitution of sex and sex-related terms for gender and vice versa; "sex confusion," the fact that any given sex variable holds many different potential meanings; and "sex obsession," the idea that the relevant variable for most inquiries related to sex/gender is sex assigned at birth. We then explore how these phenomena show up in medical machine learning research using electronic health records, with a specific focus on HIV risk prediction. Finally, we offer recommendations about how machine learning researchers can engage more carefully with questions of sex/gender.

Keywords: electronic health records; gender; healthcare; machine learning; non-binary; sex; sex/gender; transgender.

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Conflict of interest statement

The authors declare no competing interests.

Comment in

  • Responsible and accountable data science.
    Wagner B, Müller-Birn C. Wagner B, et al. Patterns (N Y). 2022 Nov 11;3(11):100629. doi: 10.1016/j.patter.2022.100629. eCollection 2022 Nov 11. Patterns (N Y). 2022. PMID: 36419445 Free PMC article. No abstract available.

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