Researchers: Jason Leigh, Stuart Bailey, Kartik Ganeshan, Robert Grossman
The Health Care field is data rich, but information poor. Data exists from admissions, laboratory, radiology, and pharmacy systems, as well as from third-party systems, which consolidate data from a variety of sources. However, for a number of reasons, these have remained “islands of information.” With the rise in implementation of clinical data repositories (one such repository is being built at the UIC Hospital), where transaction data is stored in a normalized relational format at the enterprise level, there is the promise of:
Obtaining information from such repositories has been through the conventional use of SQL or forms-driven queries. These types of queries rely on the inquirer knowing up front what information he / she wants back. These commercial packages do not detect “hidden” patterns (i.e., ones that have not been thought of before). Given the complexity of the clinical, demographic and financial data in the repository, data-mining techniques can extract information that is missed by traditional query techniques. To augment this process, the results of the data-mining algorithms (decision trees) are visualized by generating a VRML1 model which is quickly imported into LIMBO for collaborative viewing. Although this was originally intended as a tool for understanding the decisions made by data-mining algorithms, it has currently found a greater use in debugging the algorithms. The added dimension of stereoscopic 3D graphics allows one to visualize more data than is normally possible in a tree drawn on a flat display. The collaborative capabilities allows one to discuss these trees with other researchers.
This project is a collaboration between the Electronic Visualization Laboratory and the National Center for Data Mining at the University of Illinois at Chicago, directed by Robert Grossman.
Date: September 1, 1998 - December 1, 1999