title: Quality evaluation of cancer study Common Data Elements using the UMLS Semantic Network.
abstract: The binding of controlled terminology has been regarded as important for standardization of Common Data Elements (CDEs) in cancer research. However, the potential of such binding has not yet been fully explored, especially its quality assurance aspect. The objective of this study is to explore whether there is a relationship between terminological annotations and the UMLS Semantic Network (SN) that can be exploited to improve those annotations. We profiled the terminological concepts associated with the standard structure of the CDEs of the NCI Cancer Data Standards Repository (caDSR) using the UMLS SN. We processed 17798 data elements and extracted 17526 primary object class/property concept pairs. We identified dominant semantic types for the categories "object class" and "property" and determined that the preponderance of the instances were disjoint (i.e. the intersection of semantic types between the two categories is empty). We then performed a preliminary evaluation on the data elements whose asserted primary object class/property concept pairs conflict with this observation - where the semantic type of the object class fell into a SN category typically used by property or visa-versa. In conclusion, the UMLS SN based profiling approach is feasible for the quality assurance and accessibility of the cancer study CDEs. This approach could provide useful insight about how to build mechanisms of quality assurance in a meta-data repository.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, United States. Electronic address: Jiang.Guoqian@mayo.edu.
authors.forename: Guoqian
authors.initials: G
authors.lastname: Jiang
authors.affiliation: Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, United States.
authors.forename: Harold R
authors.initials: HR
authors.lastname: Solbrig
authors.affiliation: Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, United States.
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
mesh_terms: D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D009369:Neoplasms; D011786:Quality Control; D012660:Semantics; D017432:Unified Medical Language System; D018875:Vocabulary, Controlled