CRENO: An ontology to model concepts relating to culture, race, ethnicity, and nationality for health data.


Journal

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
ISSN: 2153-4063
Titre abrégé: AMIA Jt Summits Transl Sci Proc
Pays: United States
ID NLM: 101539486

Informations de publication

Date de publication:
2023
Historique:
medline: 23 6 2023
pubmed: 23 6 2023
entrez: 23 6 2023
Statut: epublish

Résumé

Generating categories and classifications is a common function in life science research; however, categorizing the human population based on "race" remains controversial. There is an awareness and recognition of social-economic disparities with respect to health which are sometimes impacted by someone's ethnicity or race. This work describes an endeavor to develop a computable ontology model to represent a standardization of the concepts surrounding culture, race, ethnicity, and nationality - concepts misrepresented widely. We constructed an OWL ontology based on reliable resources with iterative human expert evaluations and aligned it to existing biomedical ontological models. The effort produced a preliminary ontology that expresses concepts related to classes of ethnic, racial, national, and cultural identities and showcases how health disparity data can be linked and expressed within our ontological framework. Future work will explore automated methods to expand the ontology and its utilization for clinical informatics.

Identifiants

pubmed: 37350894
pii: 2308
pmc: PMC10283130

Types de publication

Journal Article

Langues

eng

Pagination

398-407

Informations de copyright

©2023 AMIA - All rights reserved.

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Auteurs

Eloisa Nguyen (E)

Seattle Pacific University, Seattle, WA.

Muhammad Amith (M)

University of North Texas, Denton, TX.

Anne Nordberg (A)

University of Texas, Arlington, TX.

Lu Tang (L)

Texas A&M University, College Station, TX.

Marcelline R Harris (MR)

University of Michigan, Ann Arbor, MI.

Cui Tao (C)

University of Texas Health Science Center at Houston, Houston, TX.

Classifications MeSH