Development and Evaluation of an Ontology for Non-Invasive Respiratory Support in Acute Care

TL;DR

This paper presents the development and evaluation of a comprehensive ontology for non-invasive respiratory support in acute care, aiming to improve data clarity, interoperability, and clinical decision-making.

Contribution

It introduces a novel OWL-based NIRS ontology with rule-based reasoning capabilities, addressing the lack of a unified structure for NIRS data and practices.

Findings

Ontology supports rule-based reasoning and therapy recommendations.

Evaluation with patient scenarios confirms the ontology's reasoning capabilities.

The framework enhances data standardization and clinical documentation.

Abstract

Managing patients with respiratory failure increasingly involves noninvasive respiratory support (NIRS) strategies to support respiration, often preventing the need for invasive mechanical ventilation. However, despite the rapidly expanding use of NIRS, there remains a significant challenge to its optimal use across all medical circumstances. It lacks a unified ontological structure, complicating guidance on NIRS modalities across healthcare systems. This study introduced NIRS ontology to support knowledge representation in acute care settings by providing a unified framework that enhances data clarity and interoperability, laying the groundwork for future clinical decision-making. We developed NIRS ontology using the Web Ontology Language (OWL) 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 a sample of 6 patient scenarios and used SPARQL queries to retrieve and test targeted inferences. The ontology has 145 classes, 11 object properties, and 18 data properties across 949 axioms that establish concept relationships. To standardize clinical concepts, we added 392 annotations, including descriptive definitions based on controlled vocabularies. SPARQL query evaluations across clinical scenarios confirmed the ontology ability 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. In conclusion, we unified NIRS concepts into an ontological framework and demonstrated its applicability through the evaluation of patient scenarios and alignment with standardized vocabularies.