Key result
New Hypertension Ontology structures clinical data to improve knowledge discovery and data sharing.
Why the study?
The study was designed to develop a hypertension ontology to improve data integration, knowledge representation, and decision-making in hypertension management.
Systematic Review
The newly developed Hypertension Ontology provides a standardized framework of 114 classes to improve data integration and knowledge representation in hypertension management.
May support hypertension data integration; leaves open validation and clinical impact in prospective studies.
Objective: Therefore, the objective of this study was to design hypertension ontology with the aim of improving integration of data, representation of knowledge and better decision making in hypertension management. Methods: First, we performed a systematic literature review (SLR) across ten different databases aiming at capturing the essential concepts. Using this extracted data a Hypertension Ontology (HPO) was created in Protégé which was loaded and published on BioPortal for availability. Results: HPO contains 114 classes and five properties that structure the risk factors, symptoms, diagnosis, and treatment. It improves knowledge discovery, data sharing capabilities, and surpasses existing hypertension-related ontologies in functionality. Conclusion: HPO facilitates standardized hypertension management in a research context and clinical practices. Next Steps will be integration of Real-world data and Interoperability to HER.
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Prity et al. (2025) conducted a systematic review in Hypertension. Hypertension Ontology (HPO) was evaluated on Development of an ontology with classes and properties. A Hypertension Ontology (HPO) containing 114 classes and five properties was developed to structure risk factors, symptoms, diagnosis, and treatment, improving knowledge discovery and data sharing.
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