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Trace clustering has increasingly been applied to find homogenous processexecutions. However, current techniques have difficulties in finding ameaningful and insightful clustering of patients on the basis of healthcaredata. The resulting clusters are often not in line with those of medicalexperts, nor do the clusters guarantee to help return meaningful process mapsof patients' clinical pathways. After all, a single hospital may conductthousands of distinct activities and generate millions of events per year. Inthis paper, we propose a novel trace clustering approach by using sample setsof patients provided by medical experts. More specifically, we learn frequentsequence patterns on a sample set, rank each patient based on the patterns, anduse an automated approach to determine the corresponding cluster. We find eachcluster separately, while the frequent sequence patterns are used to discover aprocess map. The approach is implemented in ProM and evaluated using a largedata set obtained from a university medical center. The evaluation showsF1-scores of 0.7 for grouping kidney injury, 0.9 for diabetes, and 0.64 forhead/neck tumor, while the process maps show meaningful behavioral patterns ofthe clinical pathways of these groups, according to the domain experts.

Trace Clustering on Very Large Event Data in Healthcare Using Frequent Sequence Patterns