  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.
