DISIT, Computer Science Institute, Università del Piemonte Orientale, Viale Michel 11, I-15121 Alessandria, Italy.
Department of Computer Science, Università di Torino, Italy.
J Biomed Inform. 2018 Jul;83:10-24. doi: 10.1016/j.jbi.2018.05.012. Epub 2018 May 21.
Many medical information systems record data about the executed process instances in the form of an event log. In this paper, we present a framework, able to convert actions in the event log into higher level concepts, at different levels of abstraction, on the basis of domain knowledge. Abstracted traces are then provided as an input to trace comparison and semantic process discovery. Our abstraction mechanism is able to manage non trivial situations, such as interleaved actions or delays between two actions that abstract to the same concept. Trace comparison resorts to a similarity metric able to take into account abstraction phase penalties, and to deal with quantitative and qualitative temporal constraints in abstracted traces. As for process discovery, we rely on classical algorithms embedded in the framework ProM, made semantic by the capability of abstracting the actions on the basis of their conceptual meaning. The approach has been tested in stroke care, where we adopted abstraction and trace comparison to cluster event logs of different stroke units, to highlight (in)correct behavior, abstracting from details. We also provide process discovery results, showing how the abstraction mechanism allows to obtain stroke process models more easily interpretable by neurologists.
许多医疗信息系统以事件日志的形式记录执行过程实例的数据。在本文中，我们提出了一个框架，能够基于领域知识将事件日志中的操作转换为不同抽象层次的更高层次概念。抽象后的痕迹被用作痕迹比较和语义流程发现的输入。我们的抽象机制能够处理非平凡的情况，例如交错的操作或抽象到同一概念的两个操作之间的延迟。痕迹比较采用一种能够考虑抽象阶段惩罚的相似性度量，并处理抽象痕迹中的定量和定性时间约束。至于流程发现，我们依赖于框架 ProM 中嵌入的经典算法，通过基于操作的概念意义进行抽象的能力使其具有语义。该方法已在中风护理中进行了测试，我们采用抽象和痕迹比较来对不同中风单元的事件日志进行聚类，以突出（非）正确行为，从细节中抽象出来。我们还提供了流程发现结果，展示了抽象机制如何允许获得更易于神经科医生解释的中风流程模型。
