Improving Pattern Detection in Healthcare Process Mining using an Interval-based Event Selection Method

摘要

Clinical pathways are highly variable and although many patients may follow similar pathway each individual will experience a unique set of events, for example with multiple repeated activities or varied sequences of activities. Pro-cess mining techniques are able to discover generalizable pathways based on data mining of event logs but using process mining techniques on a raw clinical pathway data to discover underlying healthcare processes is challenging due to this high variability. This paper involves two main contributions to healthcare process mining. The first contribution is developing a novel approach for event selection and outlier removing in order to improve pattern detection and thus representational quality. The second contribution is to demonstrate a new open access medical dataset, the MIMIC-III (Medical Information Mart for Intensive Care) database, which has not been used in process mining publications. In this paper, we developed a new method for variations reduction in clinical pathways data. Variation can result from outlier events that prevent capturing clear patterns. Our approach targets the behavior of repeated activities. It uses interval-based patterns to determine outlier threshold based on the time of events occurring and the distinctive attribute of observed events. The approach is tested on clinical pathways data for diabetes patients with congestive heart failure extracted from the MIMIC-III medical database and an-alyzed using the ProM process mining tool. The method has improved model precision conformance without reducing model fitness. We were able to reduce the number of events while making sure the mainstream patterns were unaffected. We found that some activity types had a large number of outlier events whereas other activities had a relatively few. The interval-based event selection method has the potential of improve process visualization. This approach is undergoing implementation as an event log enhancement technique in the ProM tool.