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

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

*Corresponding author for this work

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

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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 languageEnglish
Title of host publicationBusiness Process Management
Subtitle of host publication17th International Conference, BPM 2019, Vienna, Austria, September 1–6, 2019, Proceedings
EditorsThomas Hildebrandt, Boudewijn F. van Dongen, Maximilian Röglinger, Jan Mendling
PublisherSpringer Verlag
Number of pages18
ISBN (Electronic)9783030266196
ISBN (Print)9783030266189
Publication statusPublished - 2019
Event17th International Conference on Business Process Management, BPM 2019 - Vienna, Austria
Duration: 1 Sept 20196 Sept 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Business Process Management, BPM 2019


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


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