Center for Medical Statistics, Informatics, and Intelligent Systems (CeMSIIS), Medical University of Vienna, Spitalgasse 23, 1010 Vienna, Austria.
Research Department of Advanced Information Systems and Technology, University of Applied Sciences Upper Austria, Softwarepark 13, 4232 Hagenberg, Austria.
Int J Environ Res Public Health. 2018 Dec 10;15(12):2809. doi: 10.3390/ijerph15122809.
Process mining is a relatively new discipline that helps to discover and analyze actual process executions based on log data. In this paper we apply conformance checking techniques to the process of surveillance of melanoma patients. This process consists of recurring events with time constraints between the events.
The goal of this work is to show how existing clinical data collected during melanoma surveillance can be prepared and pre-processed to be reused for process mining.
We describe an approach based on time boxing to create process models from medical guidelines and the corresponding event logs from clinical data of patient visits.
Event logs were extracted for 1023 patients starting melanoma surveillance at the Department of Dermatology at the Medical University of Vienna between January 2010 and June 2017. Conformance checking techniques available in the ProM framework and explorative applied process mining techniques were applied.
The presented time boxing enables the direct use of existing process mining frameworks like ProM to perform process-oriented analysis also with respect to time constraints between events.
流程挖掘是一门相对较新的学科，它可以帮助根据日志数据发现和分析实际的流程执行情况。在本文中，我们将一致性检查技术应用于黑色素瘤患者监测的流程中。这个流程由具有时间约束的重复事件组成。
本工作的目标是展示如何准备和预处理在黑色素瘤监测过程中收集的现有临床数据，以便重复使用流程挖掘。
我们描述了一种基于时间框的方法，用于从医疗指南和来自于患者就诊临床数据的事件日志中创建流程模型。
从 2010 年 1 月至 2017 年 6 月期间在维也纳医科大学皮肤科开始进行黑色素瘤监测的 1023 名患者中提取了事件日志。应用了 ProM 框架中提供的一致性检查技术和探索性应用的流程挖掘技术。
所提出的时间框使直接使用现有的流程挖掘框架（如 ProM）成为可能，从而也可以针对事件之间的时间约束执行面向流程的分析。
