Correlation Engine 2.0
Clear Search sequence regions

  • adults (2)
  • data analysis (1)
  • data sources (1)
  • fall risk (9)
  • falls (3)
  • humans (1)
  • Sizes of these terms reflect their relevance to your search.

    Ontologies serve as comprehensive frameworks for organizing domain-specific knowledge, offering significant benefits for managing clinical data. This study presents the development of the Fall Risk Management Ontology (FRMO), designed to enhance clinical text mining, facilitate integration and interoperability between disparate data sources, and streamline clinical data analysis. By representing major entities within the fall risk management domain, the FRMO supports the unification of clinical language and decision-making processes, ultimately contributing to the prevention of falls among older adults. We used Ontology Web Language (OWL) to build the FRMO in Protégé. Of the seven steps of the Stanford approach, six steps were utilized in the development of the FRMO: (1) defining the domain and scope of the ontology, (2) reusing existing ontologies when possible, (3) enumerating ontology terms, (4) specifying the classes and their hierarchy, (5) defining the properties of the classes, and (6) defining the facets of the properties. We evaluated the FRMO using four main criteria: consistency, completeness, accuracy, and clarity. The developed ontology comprises 890 classes arranged in a hierarchical structure, including six top-level classes with a total of 43 object properties and 28 data properties. FRMO is the first comprehensively described semantic ontology for fall risk management. Healthcare providers can use the ontology as the basis of clinical decision technology for managing falls among older adults. © 2024. The Author(s).


    Fatimah Altuhaifa, Dalal Al Tuhaifa. Developing an Ontology Representing Fall Risk Management Domain Knowledge. Journal of medical systems. 2024 Apr 25;48(1):47

    Expand section icon Mesh Tags

    PMID: 38662184

    View Full Text