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Title page for ETD etd-07152014-074713

Type of Document Master's Thesis
Author Sulieman, Lina Mahmoud
URN etd-07152014-074713
Title A process modeling strategy to learn ischemic stroke treatment patterns from electronic medical records
Degree Master of Science
Department Biomedical Informatics
Advisory Committee
Advisor Name Title
Bradley A. Malin Committee Chair
Daniel Fabbri Committee Member
Jeremy Warner Committee Member
Nancy Lorenzi Committee Member
  • MSA
  • multiple sequence alignment
  • treatment
  • sequence
  • frequent pattern mining
  • process mining
  • workflow mining
  • clinical workflow
  • clinical pathway
Date of Defense 2014-07-01
Availability unrestricted
Process mining corresponds to a collection of methodologies designed to extract knowledge from event logs (e.g., time-stamped events) and provide a description about the underlying processes of a system. Various approaches have been developed and successfully applied to characterize, as well assess the efficiency of, the processes in traditional information management systems. In many instances, the clinical setting can be represented as a sequence of events that are aligned to deliver the best outcome. As such, to date, there have been several attempts to apply process mining techniques to learn and describe clinical workflows by learning frequent patterns from the event logs of electronic medical record (EMR) systems. However, the existing sets of techniques are designed to work with highly-structured data and systematic processes, such as those that occur immediately before and after a surgery. As such, the existing set of clinical processes that can be learned via such methods are limited in that they are 1) cumbersome and very detailed which will be difficult to read and analyze, 2) and fail to describe the actions invoked to treat subpopulations within a cohort of patients admitted for the same disease.

This thesis introduces a multi-step process mining strategy, called Treatment Mining using Frequent Sequential Patterns (TM-FSP), to learn clinical workflows from high-dimensional patient episodes. TM-FSP filters the time-ordered sets of medication classes and laboratory test types into frequent events to represent the data in a lower-dimensional form. Next, patient event sequences are subject to a multiple sequence alignment strategy and clustered based on the similarity of their aligned event patterns. Finally, the common actions for each cluster are extracted and reported as workflows. We evaluated TM-FSP with a cohort of 133 patients diagnosed with ischemic stroke at the Vanderbilt University Medical Center. The results illustrate that 7 medications and 12 laboratory test forms 2,020 patterns that are associated with the treatment of this cohort. Moreover, it was discovered that subgroups of patients, who are influenced by lipid metabolism disorders lead to variation in their treatment by excluding Beta blockers and Insulin from their treatment course.

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