Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO)

Author:

Dang Yifang1ORCID,Li Fang1ORCID,Hu Xinyue1,Keloth Vipina K12,Zhang Meng1,Fu Sunyang13ORCID,Amith Muhammad F1456ORCID,Fan J Wilfred3,Du Jingcheng1ORCID,Yu Evan1,Liu Hongfang13ORCID,Jiang Xiaoqian1ORCID,Xu Hua12ORCID,Tao Cui1

Affiliation:

1. School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, Texas, USA

2. Section of Biomedical Informatics and Data Science, Yale School of Medicine , New Haven, Connecticut, USA

3. Department of Artificial Intelligence and Informatics, Mayo Clinic , Rochester, Minnesota, USA

4. Department of Information Science, University of North Texas , Denton, Texas, USA

5. Department of Biostatistics and Data Science, School of Population Health, University of Texas Medical Branch, Galveston, Texas, USA

6. Department of Internal Medicine, John Sealy School of Medicine, University of Texas Medical Branch, Galveston, Texas, USA

Abstract

Abstract Objective Social determinants of health (SDoH) play critical roles in health outcomes and well-being. Understanding the interplay of SDoH and health outcomes is critical to reducing healthcare inequalities and transforming a “sick care” system into a “health-promoting” system. To address the SDOH terminology gap and better embed relevant elements in advanced biomedical informatics, we propose an SDoH ontology (SDoHO), which represents fundamental SDoH factors and their relationships in a standardized and measurable way. Material and Methods Drawing on the content of existing ontologies relevant to certain aspects of SDoH, we used a top-down approach to formally model classes, relationships, and constraints based on multiple SDoH-related resources. Expert review and coverage evaluation, using a bottom-up approach employing clinical notes data and a national survey, were performed. Results We constructed the SDoHO with 708 classes, 106 object properties, and 20 data properties, with 1,561 logical axioms and 976 declaration axioms in the current version. Three experts achieved 0.967 agreement in the semantic evaluation of the ontology. A comparison between the coverage of the ontology and SDOH concepts in 2 sets of clinical notes and a national survey instrument also showed satisfactory results. Discussion SDoHO could potentially play an essential role in providing a foundation for a comprehensive understanding of the associations between SDoH and health outcomes and paving the way for health equity across populations. Conclusion SDoHO has well-designed hierarchies, practical objective properties, and versatile functionalities, and the comprehensive semantic and coverage evaluation achieved promising performance compared to the existing ontologies relevant to SDoH.

Funder

National Institute on Aging

National Human Genome Research Institute

NHGRI

National Institute of Allergy and Infectious Diseases

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Reference50 articles.

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