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非常规医院感染暴发事件情景表达:基于知识元模型构建

Scenario expression of unconventional healthcare-associated infection outbreak events: construction based on knowledge element model

  • 摘要:
    目的 构建反映非常规医院感染暴发事件客观状态的情景表达模型,为医院决策者制定针对性防控策略提供依据。
    方法 基于公共安全“三角形”框架与灾害学理论划分非常规医院感染暴发事件的情景构成要素,运用知识元的理论方法对各情景构成要素进行结构化表达。
    结果 非常规医院感染暴发事件的情景构成要素划分为:致灾因子(DF)、承灾体(DB)、孕灾环境(DE)及应急活动(EA);根据知识元二元组形式构建非常规医院感染暴发事件的空间、时间表达模型,并对事件的部分情景进行表达示例,最终提出非常规医院感染暴发事件“五维”情景表达模型,包括:构成要素维(K)、含义维(N)、特征维(P)、特征值维(D)和时间维(T)。
    结论 通过知识元理论的跨域应用,为非常规医院感染暴发事件的情景表达提供创新模型示范,对提升医院感染应急准备工作能力具有重要意义。

     

    Abstract:
    Objective To construct a scenario expression model reflecting the objective state of unconventional healthcare-associated infection outbreak events, providing a basis for hospital decision-makers to formulate targeted prevention and control strategies.
    Methods On the basis of the "triangular" framework of public safety and the theory of disaster science, the components of scenarios of unconventional healthcare-associated infection outbreak events were classified, and the theoretical method of knowledge elements was used to structurally express each component.
    Results The scenario components of unconventional healthcare-associated infection outbreak events were classified into disaster-causing factors (DF), disaster-bearing body (DB), disaster-prone environment (DE), and emergency activities (EA). The spatial and temporal expression models of unconventional healthcare-associated infection outbreak events were constructed according to the binary form of knowledge elements, and some scenarios of the events were expressed as examples. Finally, the five-dimensional scenario expression model of unconventional healthcare-associated infection outbreak events was proposed, consisting of component (K), denotation (N), feature (P), value (D), and time (T) dimensions.
    Conclusions The cross-domain application of the method of knowledge elements provides an innovative model demonstration for unconventional healthcare-associated infection outbreak events, which is of great significance for improving the preparedness for healthcare-associated infection emergencies.

     

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