The National Cancer Institute Enterprise Vocabulary Services (NCI EVS) uses a wide range of quality assurance (QA) techniques to maintain and extend NCI Thesaurus (NCIt). NCIt is a reference terminology and biomedical ontology used in a growing number of NCI and other systems that extend from translational and basic research through clinical care to public information and administrative activities. Both automated and manual QA techniques are employed throughout the editing and publication cycle, which includes inserting and editing NCIt in NCI Metathesaurus. NCI EVS conducts its own additional periodic and ongoing content QA. External reviews, and extensive evaluation by and interaction with EVS partners and other users, have also played an important part in the QA process. There have always been tensions and compromises between meeting the needs of dependent systems and providing consistent and well-structured content; external QA and feedback have been important in identifying and addressing such issues. Currently, NCI EVS is exploring new approaches to broaden external participation in the terminology development and QA process.
Aboriginal youth is one of the fastest growing of all populations in Saskatchewan today. This is a prime group to target for training in the health care professions. The need for strategies to support recruitment and retention in these programs is critical for maintaining our present standard and increasing demands of quality health care. Program initiatives and supports need to be implemented to encourage this population to enroll in and complete health care programs. Although only 5 years old, the University of Saskatchewan, First Nations University of Canada, and Saskatchewan Institute of Applied Science and Technology (SIAST) have created a viable northern nursing program with a retention rate of Aboriginal postsecondary students 13% greater than the provincial norm. They graduated their first class of nursing students from and for the North, May 2006.
There are thousands of distinct disease entities and concepts, each of which are known by different and sometimes contradictory names. The lack of a unified system for managing these entities poses a major challenge for both machines and humans that need to harmonize information to better predict causes and treatments for disease. The Mondo Disease Ontology is an open, community-driven ontology that integrates key medical and biomedical terminologies, supporting disease data integration to improve diagnosis, treatment, and translational research. Mondo records the sources of all data and is continually updated, making it suitable for research and clinical applications that require up-to-date disease knowledge.
PURPOSE To audit and improve the completeness of the hierarchic (or is-a) relations of the National Cancer Institute (NCI) Thesaurus to support its role as a faceted system for querying cancer registry data. METHODS We performed quality auditing of the 19.01d version of the NCI Thesaurus. Our hybrid auditing method consisted of three main steps: computing nonlattice subgraphs, constructing lexical features for concepts in each subgraph, and performing subsumption reasoning with each subgraph to automatically suggest potentially missing is-a relations. RESULTS A total of 9,512 nonlattice subgraphs were obtained. Our method identified 925 potentially missing is-a relations in 441 nonlattice subgraphs; 72 of 176 reviewed samples were confirmed as valid missing is-a relations and have been incorporated in the newer versions of the NCI Thesaurus. CONCLUSION Autosuggested changes resulting from our auditing method can improve the structural organization of the NCI Thesaurus in supporting its new role for faceted query.
Background The National Cancer Institute (NCI) Thesaurus provides reference terminology for NCI and other systems. Previously, we proposed a hybrid prototype utilizing lexical features and role definitions of concepts in non-lattice subgraphs to identify missing IS-A relations in the NCI Thesaurus. However, no domain expert evaluation was provided in our previous work. In this paper, we further enhance the hybrid approach by leveraging a novel lexical feature—roots of noun chunks within concept names. Formal evaluation of our enhanced approach is also performed. Method We first compute all the non-lattice subgraphs in the NCI Thesaurus. We model each concept using its role definitions, words and roots of noun chunks within its concept name and its ancestor’s names. Then we perform subsumption testing for candidate concept pairs in the non-lattice subgraphs to automatically detect potentially missing IS-A relations. Domain experts evaluated the validity of these relations. Results We applied our approach to 19.08d version of the NCI Thesaurus. A total of 55 potentially missing IS-A relations were identified by our approach and reviewed by domain experts. 29 out of 55 were confirmed as valid by domain experts and have been incorporated in the newer versions of the NCI Thesaurus. 7 out of 55 further revealed incorrect existing IS-A relations in the NCI Thesaurus. Conclusions The results showed that our hybrid approach by leveraging lexical features and role definitions is effective in identifying potentially missing IS-A relations in the NCI Thesaurus.
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