Temporal detection and analysis of guideline interactions
Temporal detection and analysis of guideline interactions
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DOI：
10.1016/j.artmed.2017.01.001
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发表时间：
2017-02-01
影响因子：
7.5
通讯作者：
Terenziani, Paolo
中科院分区：
文献类型：
--
作者：
Anselma, Luca;Piovesan, Luca;Terenziani, Paolo
背景资料：临床实践指南（CPG）在医疗领域发挥着重要作用，以保证医疗援助的质量，为医生提供基于证据的单一病理治疗干预信息。受多种疾病影响的患者（共病患者）的治疗是现代医疗保健的主要挑战之一。它需要开发新的方法，支持医生治疗CPG之间的相互作用。几种方法已经开始面对这样一个具有挑战性的问题。然而，它们有一个很大的局限性：它们没有考虑到时间方面。事实上，实际上，相互作用发生在时间中。例如，根据不同指导方针采取的两个行动的效果可能会发生潜在冲突，但只有当此类行动的执行时间使其效果在时间上重叠时，才会发生实际冲突。目标：我们的目标是设计一种方法来检测和分析考虑时间维度的CPG之间的交互作用。方法：在本文中，我们首先扩展了我们以前的本体模型来处理的事实，即行动，目标，效果和相互作用发生的时间，并建模它们之间的定性和定量的时间约束。然后，我们确定不同的应用场景，并为他们中的每一个，我们提出了不同类型的用户医生的设施，有用的支持时间检测interactions.Results：我们提供了一个模块化的方法，在不同的人工智能时间推理技术，时间约束传播的基础上，被广泛利用，为用户提供这样的设施。我们应用我们的方法，以两种情况下的合并症，使用简化版本的CPGs.Conclusion：我们提出了一种创新的方法来检测和分析CPG之间的相互作用，考虑到不同来源的时间信息（CPG，本体知识和执行日志），这是第一个在文献中，考虑到时间的问题，并考虑到不同的应用场景。(C)2017 Elsevier B.V.版权所有。
Background: Clinical practice guidelines (CPGs) are assuming a major role in the medical area, to grant the quality of medical assistance, supporting physicians with evidence-based information of interventions in the treatment of single pathologies. The treatment of patients affected by multiple diseases (comorbid patients) is one of the main challenges for the modern healthcare. It requires the development of new methodologies, supporting physicians in the treatment of interactions between CPGs. Several approaches have started to face such a challenging problem. However, they suffer from a substantial limitation: they do not take into account the temporal dimension. Indeed, practically speaking, interactions occur in time. For instance, the effects of two actions taken from different guidelines may potentially conflict, but practical conflicts happen only if the times of execution of such actions are such that their effects overlap in time.Objectives: We aim at devising a methodology to detect and analyse interactions between CPGs that considers the temporal dimension.Methods: In this paper, we first extend our previous ontological model to deal with the fact that actions, goals, effects and interactions occur in time, and to model both qualitative and quantitative temporal constraints between them. Then, we identify different application scenarios, and, for each of them, we propose different types of facilities for user physicians, useful to support the temporal detection of interactions.Results: We provide a modular approach in which different Artificial Intelligence temporal reasoning techniques, based on temporal constraint propagation, are widely exploited to provide users with such facilities. We applied our methodology to two cases of comorbidities, using simplified versions of CPGs.Conclusion: We propose an innovative approach to the detection and analysis of interactions between CPGs considering different sources of temporal information (CPGs, ontological knowledge and execution logs), which is the first one in the literature that takes into account the temporal issues, and accounts for different application scenarios. (C) 2017 Elsevier B.V. All rights reserved.