Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety of data collected from healthcare processes: operational processes given by claims data, a collection of events during surgery, data related to pre-operative and post-operative care, and high-level data collections based on regular ambulant visits with no apparent events. In this case study, a data set from the last category is analyzed. We apply process-mining techniques on sparse patient heart failure data and investigate whether an information gain towards several research questions is achievable. Here, available data are transformed into an event log format, and process discovery and conformance checking are applied. Additionally, patients are split into different cohorts based on comorbidities, such as diabetes and chronic kidney disease, and multiple statistics are compared between the cohorts. Conclusively, we apply decision mining to determine whether a patient will have a cardiovascular outcome and whether a patient will die.

翻译：医疗领域的过程挖掘在处理不同数据类型时面临诸多挑战。医疗过程收集的数据具有高度多样性：包括来自索赔数据的手术流程数据、手术过程中的事件集合、术前与术后护理相关数据，以及基于定期门诊随访（无明显事件记录）的高层次数据集合。本案例研究针对最后一类数据集展开分析。我们采用过程挖掘技术处理稀疏的心力衰竭患者数据，探究能否为多个研究问题获取信息增益。通过将现有数据转换为事件日志格式，应用过程发现与合规性检测方法。此外，根据合并症（如糖尿病与慢性肾病）将患者分为不同队列，并比较队列间的多项统计指标。最终，我们运用决策挖掘技术判断患者是否会发生心血管结局或死亡。