标题： NIRS：急症护理中非侵入性呼吸支持的本体论 显示英文标题

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

摘要： 目标：开发一种非侵入性呼吸支持（NIRS）本体，以支持急症护理环境中的知识表示。 材料和方法：我们使用网络本体语言（OWL）语义和Protege开发了NIRS本体，以组织临床概念和关系。 为了在层次结构之外实现基于规则的临床推理，我们添加了语义网规则语言（SWRL）规则。 我们通过添加17个假设的患者临床情景来评估逻辑推理。 我们使用SPARQL查询和电子重症监护单元（eICU）协作研究数据库的数据来检索和测试目标推断。 结果：本体包含132个类，12个对象属性和17个数据属性，分布在882个公理中，用于建立概念之间的关系。 为了标准化临床概念，我们添加了350个注释，包括基于受控词汇表的描述性定义。 SPARQL查询成功通过检索适当的患者结果验证了所有测试案例（规则），例如，因急性呼吸衰竭接受高流量鼻导管（HFNC）治疗2小时的患者可能避免气管插管。 讨论：NIRS本体正式表示了特定领域的概念，包括通气模式、患者特征、治疗参数和结果。 对临床情景的SPARQL查询评估证实了本体支持基于规则的推理和治疗建议的能力，为一致的文档记录实践、整合到临床数据模型中以及NIRS结果的高级分析提供了基础。 结论：我们将NIRS概念统一到本体框架中，并通过假设患者情景的评估和与标准化词汇的一致性展示了其适用性。 显示英文摘要

摘要： 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.