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

Xixi Lu, Seyed Amin Tabatabaei, Mark Hoogendoorn, Hajo A. Reijers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


    Trace clustering has increasingly been applied to find homogenous process executions. However, current techniques have difficulties in finding a meaningful and insightful clustering of patients on the basis of healthcare data. The resulting clusters are often not in line with those of medical experts, nor do the clusters guarantee to help return meaningful process maps of patients’ clinical pathways. After all, a single hospital may conduct thousands of distinct activities and generate millions of events per year. In this paper, we propose a novel trace clustering approach by using sample sets of patients provided by medical experts. More specifically, we learn frequent sequence patterns on a sample set, rank each patient based on the patterns, and use an automated approach to determine the corresponding cluster. We find each cluster separately, while the frequent sequence patterns are used to discover a process map. The approach is implemented in ProM and evaluated using a large data set obtained from a university medical center. The evaluation shows F1-scores of 0.7 for grouping kidney injury, 0.9 for diabetes, and 0.64 for head/neck tumor, while the process maps show meaningful behavioral patterns of the clinical pathways of these groups, according to the domain experts.
    Original languageUndefined/Unknown
    Title of host publicationBusiness Process Management
    Subtitle of host publication17th International Conference, BPM 2019, Vienna, Austria, September 1–6, 2019, Proceedings
    EditorsThomas Hildebrandt
    Place of PublicationCham
    Number of pages18
    ISBN (Electronic)9783030266196
    ISBN (Print)9783030266202
    Publication statusPublished - 2019

    Publication series

    NameLecture Notes in Computer Science


    • Trace clustering
    • Frequent sequential patterns
    • Process mining
    • Machine learning

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