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

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 impacte...

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Bibliographic Details
Published in:AMIA Summits on Translational Science proceedings 2023, Vol.2023, p.398-407
Main Authors: Nguyen, Eloisa, Amith, Muhammad, Nordberg, Anne, Tang, Lu, Harris, Marcelline R, Tao, Cui
Format: Article
Language:English
Online Access:Get full text
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Summary: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.
ISSN:2153-4063