title: Identifying significant associations using free text problem summary lists in a clinical data repository.
abstract: Recent studies have been seeking common signatures in gene expression profiles in order to find novel associations. We chose to test the extensibility of a tool created to perform association analyses of gene expression signatures by applying the same technique to problem summary lists. Both commonly known as well as less known associations were found using this technique.
authors.affiliation: Department of Pediatrics and Comprehensive Cancer Center, University of Michigan Medical School, Ann Arbor, Michigan, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Hanauer
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Rhodes
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
mesh_terms: D020869:Gene Expression Profiling; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008501:Medical Records, Problem-Oriented