Abstract：
Clinical practice guidelines (CPGs) are patient management tools that synthesize medical knowledge into an actionable format. CPGs are disease specific with limited applicability to the management of complex patients suffering from multimorbidity. For the management of these patients, CPGs need to be augmented with secondary medical knowledge coming from a variety of knowledge repositories. The operationalization of this knowledge is key to increasing CPGs\' uptake in clinical practice. In this work, we propose an approach to operationalizing secondary medical knowledge inspired by graph rewriting. We assume that the CPGs can be represented as task network models, and provide an approach for representing and applying codified medical knowledge to a specific patient encounter. We formally define revisions that model and mitigate adverse interactions between CPGs and we use a vocabulary of terms to instantiate these revisions. We demonstrate the application of our approach using synthetic and clinical examples. We conclude by identifying areas for future work with the vision of developing a theory of mitigation that will facilitate the development of comprehensive decision support for the management of multimorbid patients.
摘要：
临床实践指南（CPG）是将医学知识合成为可操作格式的患者管理工具。CPG是疾病特异性的，对患有多种疾病的复杂患者的管理具有有限的适用性。对于这些患者的管理，需要使用来自各种知识库的二级医学知识来增强CPG。这些知识的可操作性是临床实践中增加CPGs吸收的关键。在这项工作中,我们提出了一种在图形重写的启发下操作二级医学知识的方法。我们假设CPG可以表示为任务网络模型，并提供了一种方法来表示和应用编码的医学知识到一个特定的病人遇到。我们正式定义了对CPG之间的不利相互作用进行建模和减轻的修订，并使用术语词汇表来实例化这些修订。我们使用合成和临床实例演示了我们的方法的应用。最后，我们确定了未来工作的领域，并提出了发展缓解理论的愿景，该理论将有助于为多患者的管理提供全面的决策支持。
