Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen – such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient’s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.

具有多种医疗状况（合并症）的患者对临床决策支持系统提出了重大挑战，因为不同的《临床实践指南》（CPG）通常涉及不良相互作用，例如药物或疾病相互作用。此外，经常存在在多个CPG中优化护理和资源的机会。在现有技术中已经解决了这些挑战，许多方法集中在共病CIG的静态集成上。然而，我们观察到许多方面经常会以无法预见的方式随时间动态变化-例如护理任务的延迟，资源的可获得性，测试结果和急性合并症。为了确保集成多个共病CIG的临床安全性和有效性，必须考虑这些执行时的困难。进一步，在处理合并症时，我们指出临床医生通常会根据患者的健康状况考虑多种复杂的解决方案。因此，需要基于动态健康参数的执行时灵活性来有效，安全地应对合并症。在这项工作中，我们基于具有清晰定义的集成语义的OWL本体，引入了一种灵活的，知识驱动的和执行时的方法来进行共存CIG集成。