Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups

Objective: The objective of this study is to develop an approach to evaluate the quality of terminological annotations on the value set (ie, enumerated value domain) components of the common data elements (CDEs) in the context of clinical research using both unified medical language system (UMLS) semantic types and groups. Materials and methods: The CDEs of the National Cancer Institute (NCI) Cancer Data Standards Repository, the NCI Thesaurus (NCIt) concepts and the UMLS semantic network were integrated using a semantic web-based framework for a SPARQL-enabled evaluation. First, the set of CDE-permissible values with corresponding meanings in external controlled terminologies were isolated. The corresponding value meanings were then evaluated against their NCI- or UMLS-generated semantic network mapping to determine whether all of the meanings fell within the same semantic group. Results: Of the enumerated CDEs in the Cancer Data Standards Repository, 3093 (26.2%) had elements drawn from more than one UMLS semantic group. A random sample (n1/4100) of this set of elements indicated that 17% of them were likely to have been misclassified. Discussion: The use of existing semantic web tools can support a high-throughput mechanism for evaluating the quality of large CDE collections. This study demonstrates that the involvement of multiple semantic groups in an enumerated value domain of a CDE is an effective anchor to trigger an auditing point for quality evaluation activities. Conclusion: This approach produces a useful quality assurance mechanism for a clinical study CDE repository.