arXiv Open Access 2025

When Algorithms Infer Gender: Revisiting Computational Phenotyping with Electronic Health Records Data

Jessica Gronsbell Hilary Thurston Lillian Dong Vanessa Ferguson Diksha Sen Chaudhury +3 lainnya

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Abstrak

Computational phenotyping has emerged as a practical solution to the incomplete collection of data on gender in electronic health records (EHRs). This approach relies on algorithms to infer a patient's gender using the available data in their health record, such as diagnosis codes, medication histories, and information in clinical notes. Although intended to improve the visibility of trans and gender-expansive populations in EHR-based biomedical research, computational phenotyping raises significant methodological and ethical concerns related to the potential misuse of algorithm outputs. In this paper, we review current practices for computational phenotyping of gender and examine its challenges through a critical lens. We also highlight existing recommendations for biomedical researchers and propose priorities for future work in this domain.

Topik & Kata Kunci

cs.CY

Penulis (8)

Jessica Gronsbell

Hilary Thurston

Lillian Dong

Vanessa Ferguson

Diksha Sen Chaudhury

Braden O'Neill

Katrina S. Sha

Rebecca Bonneville

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APA MLA BibTeX

Gronsbell, J., Thurston, H., Dong, L., Ferguson, V., Chaudhury, D.S., O'Neill, B. et al. (2025). When Algorithms Infer Gender: Revisiting Computational Phenotyping with Electronic Health Records Data. https://arxiv.org/abs/2508.14150

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓