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The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach

Zhang, Qi
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261

Abstract Details

2013, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Workflow technology has broadened substantially into the healthcare industry in recent years. Hospitals and other medical units are advocating this technology as a means to improve operational efficiency, achieve patient safety goals and positively influence the quality of care. As workflow management concepts and workflow-based techniques are being applied in the healthcare information systems, there is also a growing interest in applying the data analysis and knowledge discovery techniques, such as sequential patterns mining techniques, to support the use of large healthcare information databases, which can be made more efficient when synchronized with workflow system. This thesis introduces workflow techniques and their application in Healthcare information system, addresses the opportunities that workflow technology has to make a profound impact on medical care system while examining the challenges that are presented in the healthcare arena. It also addresses the concepts for sequential pattern mining and its widely used algorithms, compares the performance of the algorithms and indicates their preferred application domains. Based on a popular approach, an improved algorithm, called the transformation–based frequent pattern growth (T-FPG), was proposed which has the potential to be more efficient in mining large sequence databases with numerous patterns and/or long patterns than other classic methods. All the features of this algorithm are illustrated, and the experimental results of the T-FPG algorithms show that it outperforms PrefixSpan, the typical FR-growth algorithm for sequential patterns mining.
Chia Han, Ph.D. (Committee Chair)
Prabir Bhattacharya, Ph.D. (Committee Member)
Anca Ralescu, Ph.D. (Committee Member)
85 p.
Computer Science
Workflow; Windows Workflow Foundation; Frequent Itemset; Sequential Patterns; Projected Database; Frequent Pattern Growth

Recommended Citations

Citations

  • Zhang, Q. (2013). The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261

    APA Style (7th edition)

  • Zhang, Qi. The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach. 2013. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261.

    MLA Style (8th edition)

  • Zhang, Qi. "The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach." Master's thesis, University of Cincinnati, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261

    Chicago Manual of Style (17th edition)

ucin1378113261
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© 2013, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.