Visualization can make explicit the subtle and interdependent relationships that exist among terms in a broad topical area. Such visualizations may help in refining retrieval or enhance the user's understanding a complex area. We will demonstrate an application consisting of a term generation service, UMLS categorization of the terms, and a spring embedded visualization of the term set relationships. Background. The SIFTER (Smart Information Filtering Technology for Electronic Resources) group at Indiana University is experimenting with software methodologies for information extraction based on automated document analysis techniques. Research has been conducted in the area of term and association discovery at IU for several years 1 . Recently, this work was expanded to include external categorization of terms into standard medical concepts by using the UMLS Metathesaurus services of the National Institute of Health 2 . The goal is to apply a visualization technique to display complex networks of relationships that may exist among different medical concepts. System. Terms are discovered using an algorithm based on an automated thesaurus generation procedure, and subsequently associations among terms are established using a method known as Maxi-min distance clustering
['Larry Mongin', 'Javed Mostafa', 'John Fieber']
Using a Spring Embedding Algorithm to Display Term Relationships from a Medical Concept Discovery System