Patients suffering from multiple diseases (multi-morbid patients) often have complex clinical pathways. They are diagnosed and treated by different specialties and undergo other clinical actions related to various diagnoses. Coordination of care for these patients is often challenging, and it would be of great benefit to get better insight into how the clinical pathways develop in reality. Discovering these pathways using traditional process mining techniques and standard event logs may be difficult because the patient is involved in several highly independent clinical processes. Our objective is to explore the potential of analyzing these pathways using an event log representation reflecting the independent clinical processes. Our main research question is: How can we identify valuable insights by using a multi-entity event data representation for clinical pathways of multi-morbid patients? Our method was built on the idea to represent multiple entities in event logs as event graphs. The MIMIC-III data-set was used to evaluate the feasibility of this approach. Several clinical entities were identified and then mapped into an event graph. Finally, multi-entity directly follows graphs were discovered by querying the event graph visualizing them. Our result shows that paths involving multiple entities include traditional process mining concepts not for one clinical process but all involved processes. In addition, the relationship between activities of different clinical processes, which was not recognizable in traditional models, is visible in the event graph representation.
翻译:患有多种疾病的病人(多病病人)往往有复杂的临床途径,他们由不同的专业诊断和治疗,并接受与各种诊断有关的其他临床行动。对这些病人的护理协调往往具有挑战性,如果能够更好地了解临床途径在现实中如何发展,将大有裨益。利用传统过程采矿技术和标准事件日志发现这些途径可能很困难,因为病人参与了几个高度独立的临床过程。我们的目标是探索利用反映独立临床过程的事件日志代表来分析这些途径的可能性。我们的主要研究问题是:我们如何利用多病病人临床路径的多点事件数据代表来发现有价值的洞察力?我们的方法建立在在事件日志中代表多个实体的设想之上,作为事件图表。MIMIC-III数据集用于评估这一方法的可行性。一些临床实体被确定并随后绘制成一个事件图表。最后,通过对事件图解图解来发现多点直接遵循的图表。我们的结果显示,涉及多个实体的多点路径包括传统过程的概念,而不是一个临床进程之间的可识别的临床进程。