Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

When algorithms infer gender: revisiting computational phenotyping with electronic health records data.

Biology of sex differences - 31 Dec 2025

Gronsbell Jessica, Thurston Hilary, Dong Lillian, Ferguson Vanessa, Chaudhury Diksha Sen, O'Neill Braden, Sha Katrina S, Bonneville Rebecca

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electronic Health Records

Share to Phenotype

Share to Sex Characteristics

Source

Open in PubMed Open full text

Identifiers

PMID: 41476311
DOI: 10.1186/s13293-025-00783-8
PMCID: PMC12865949

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

When algorithms infer gender: revisiting computational phenotyping with electronic health records data.PMID 41476311Read ↗

Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and SubgroupsSemantic proximity Read ↗

Developing and Validating a Computable Phenotype for the Identification of Transgender and Gender Nonconforming Individuals and Subgroups.Semantic proximity Read ↗

The Use of Computational Phenotypes within Electronic Healthcare Data to Identify Transgender People in the United States: A Narrative ReviewSemantic proximity Read ↗

Did I Tell You That? Ethical Issues Related to Using Computational Methods to Discover Non-Disclosed Patient Characteristics.Semantic proximity Read ↗

A Deep Learning Approach for Transgender and Gender Diverse Patient Identification in Electronic Health RecordsSemantic proximity Read ↗

Addressing sex bias in biological databases worldwideSemantic proximity Read ↗

Large-scale automated detection reveals pervasive sex imbalance in biomedical researchSemantic proximity Read ↗

The perils of premature phenotyping in COVID-19: a call for cautionSemantic proximity Read ↗

Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network.Semantic proximity Read ↗

Development and Validation of a Computable Phenotype for Turner Syndrome Utilizing Electronic Health Records from a National Pediatric NetworkSemantic proximity Read ↗

The use of electronic health records for psychiatric phenotyping and genomics.Semantic proximity Read ↗

WITHDRAWN: Gender Dysphoria Preceding Intersex Recognition Demonstrates a Biophysiological Basis for Sex IdentitySemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.