Graphical Methods for Reducing, Visualizing and Analyzing Large Data Sets Using Hierarchical Terminologies
Brown Bag Lecture by Dr. Xia Jing | 12/06/2011 11AM-12PM | 7th Floor Conference Room, Bldg 38A
Abstract: Objective: To explore new graphical methods for reducing and analyzing large data sets in which the data are coded with a hierarchical terminology.
Methods: We use a hierarchical terminology to organize a data set and display it in a graph. We reduce the size and complexity of the data set by considering the terminological structure and the data set itself (using a variety of thresholds) as well as contributions of child level nodes to parent level nodes.
Results: We found that our methods can reduce large data sets to manageable size and highlight the differences among graphs. The thresholds used as filters to reduce the data set can be used alone or in combination. We applied our methods to two data sets containing information about how nurses and physicians query online knowledge resources. The reduced graphs make the differences between the two groups readily apparent.
Conclusions: This is a new approach to reduce size and complexity of large data sets and to simplify visualization. This approach can be applied to any data sets that are coded with hierarchical terminologies.
Bio: Xia Jing joined Lister Hill National Center for Biomedical Communications at the NLM as a postdoctoral fellow in March, 2010, working with Jim Cimino. Her current projects include: graphic methods for analyzing large data sets through the use of hierarchical terminologies; development of a user-friendly tool for capturing librarian knowledge (LITE: http://lite.bmi.utah.edu/); and development of a framework the research aspects of clinical research informatics. She earned Bachelor’s degree of Medicine (subject: Medical Library and Information Sciences) from China Medical University in July, 1997, and her PhD in Health Informatics from the University of Salford in England in February, 2010. Her research interests include clinical information needs, graphic information analysis and information evaluation, linkage between genomic information and health information, intelligent systems, knowledge representation and management, translational biomedical informatics, structure and query of knowledge base.