Process-Oriented Iterative Multiple Alignment for Medical Process Mining

Shuhong Chen, Sen Yang, Moliang Zhou, Randall Burd, Ivan Marsic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.

Original languageEnglish (US)
Title of host publicationProceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
EditorsRaju Gottumukkala, George Karypis, Vijay Raghavan, Xindong Wu, Lucio Miele, Srinivas Aluru, Xia Ning, Guozhu Dong
PublisherIEEE Computer Society
Pages438-445
Number of pages8
ISBN (Electronic)9781538614808
DOIs
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2017-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Other

Other17th IEEE International Conference on Data Mining Workshops, ICDMW 2017
CountryUnited States
CityNew Orleans
Period11/18/1711/21/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

Keywords

  • Knowledge Discovery
  • Medical Healthcare Informatics
  • Process Mining
  • Trace Alignment
  • Workflow Analysis

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  • Cite this

    Chen, S., Yang, S., Zhou, M., Burd, R., & Marsic, I. (2017). Process-Oriented Iterative Multiple Alignment for Medical Process Mining. In R. Gottumukkala, G. Karypis, V. Raghavan, X. Wu, L. Miele, S. Aluru, X. Ning, & G. Dong (Eds.), Proceeding - 17th IEEE International Conference on Data Mining Workshops, ICDMW 2017 (pp. 438-445). (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2017-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2017.63