Date Available
1-19-2019
Year of Publication
2018
Document Type
Master's Thesis
Degree Name
Master of Science (MS)
College
Engineering
Department/School/Program
Computer Science
Advisor
Dr. Zongming Fei
Co-Director of Graduate Studies
Dr. Sujin Kim
Abstract
With exponential growth on a daily basis, there is potentially valuable information hidden in complex electronic medical records (EMR) systems. In this thesis, several efficient data mining algorithms were explored to discover hidden knowledge in insurance claims data. The first aim was to cluster three levels of information overload(IO) groups among chronic rheumatic disease (CRD) patient groups based on their clinical events extracted from insurance claims data. The second aim was to discover hidden patterns using three renowned pattern mining algorithms: Apriori, frequent pattern growth(FP-Growth), and sequential pattern discovery using equivalence classes(SPADE). The SPADE algorithm was found to be the most efficient method for the dataset used. Finally, a prototype system named myDietPHIL was developed to manage clinical events for CRD patients’ and visualize the relationships of frequent clinical events. The system has been tested and visualization of relationships could facilitate patient education.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2018.260
Recommended Citation
Piao, Zhenhui, "INTERACTIVE CLINICAL EVENT PATTERN MINING AND VISUALIZATION USING INSURANCE CLAIMS DATA" (2018). Theses and Dissertations--Computer Science. 70.
https://uknowledge.uky.edu/cs_etds/70
Included in
Biomedical Commons, Biomedical Engineering and Bioengineering Commons, Computer Engineering Commons, Health Information Technology Commons
