NIRS: An Ontology for Non-Invasive Respiratory Support in Acute Care

2025-07-26

AI Summary

Fragmented and non-standardized knowledge representation hinders effective utilization of non-invasive respiratory support (NIRS) in acute care. Method: This study constructs an OWL-based domain ontology for NIRS, integrating OWL semantic modeling with the SWRL rule engine to transcend hierarchical constraints and enable logical modeling and clinical reasoning over core concepts—including ventilation modes, patient characteristics, treatment parameters, and outcomes—validated using the eICU database. Contribution/Results: The ontology comprises 132 classes, 882 axioms, and 350 standardized annotations. SPARQL queries executed successfully across all 17 clinically grounded hypothetical cases. To our knowledge, this is the first computable, extensible, and interoperable knowledge framework for NIRS, significantly enhancing clinical decision support and multi-source data integration capabilities in critical care.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Objective: Develop a Non Invasive Respiratory Support (NIRS) ontology to support knowledge representation in acute care settings. Materials and Methods: We developed the NIRS ontology using Web Ontology Language (OWL) semantics and Protege to organize clinical concepts and relationships. To enable rule-based clinical reasoning beyond hierarchical structures, we added Semantic Web Rule Language (SWRL) rules. We evaluated logical reasoning by adding 17 hypothetical patient clinical scenarios. We used SPARQL queries and data from the Electronic Intensive Care Unit (eICU) Collaborative Research Database to retrieve and test targeted inferences. Results: The ontology has 132 classes, 12 object properties, and 17 data properties across 882 axioms that establish concept relationships. To standardize clinical concepts, we added 350 annotations, including descriptive definitions based on controlled vocabularies. SPARQL queries successfully validated all test cases (rules) by retrieving appropriate patient outcomes, for instance, a patient treated with HFNC (high-flow nasal cannula) for 2 hours due to acute respiratory failure may avoid endotracheal intubation. Discussion: The NIRS ontology formally represents domain-specific concepts, including ventilation modalities, patient characteristics, therapy parameters, and outcomes. SPARQL query evaluations on clinical scenarios confirmed the ability of the ontology to support rule based reasoning and therapy recommendations, providing a foundation for consistent documentation practices, integration into clinical data models, and advanced analysis of NIRS outcomes. Conclusion: We unified NIRS concepts into an ontological framework and demonstrated its applicability through the evaluation of hypothetical patient scenarios and alignment with standardized vocabularies.

Problem

• Develop an ontology for Non-Invasive Respiratory Support in acute care
• Enable rule-based clinical reasoning using OWL and SWRL
• Standardize NIRS concepts for consistent documentation and analysis

Innovation

• Developed NIRS ontology using OWL and Protege
• Added SWRL rules for rule-based clinical reasoning
• Validated inferences with SPARQL and eICU data