Objective: Quality assurance of large ontological systems such as SNOMED CT is an indispensable part of the terminology management lifecycle. We introduce a hybrid structural-lexical method for scalable and systematic discovery of missing hierarchical relations and concepts in SNOMED CT. Material and Methods: All non-lattice subgraphs (the structural part) in SNOMED CT are exhaustively extracted using a scalable MapReduce algorithm. Four lexical patterns (the lexical part) are identified among the extracted non-lattice subgraphs. Non-lattice subgraphs exhibiting such lexical patterns are often indicative of missing hierarchical relations or concepts. Each lexical pattern is associated with a potential specific type of error. Results: Applying the structural-lexical method to SNOMED CT (September 2015 US edition), we found 6801 non-lattice subgraphs that matched these lexical patterns, of which 2046 were amenable to visual inspection. We evaluated a random sample of 100 small subgraphs, of which 59 were reviewed in detail by domain experts. All the subgraphs reviewed contained errors confirmed by the experts. The most frequent type of error was missing is-a relations due to incomplete or inconsistent modeling of the concepts. Conclusions: Our hybrid structural-lexical method is innovative and proved effective not only in detecting errors in SNOMED CT, but also in suggesting remediation for these errors. Key words: SNOMED CT, ontology, quality assurance, non-lattice subgraph OBJECTIVES SNOMED CT is the most comprehensive clinical health care terminology worldwide, and its use is mandated in the United States as a part of the Meaningful Use incentive program. Quality assurance is an indispensable part of the lifecycle management of biomedical terminologies, including SNOMED CT. 1 However, quality assurance of such a large terminology system is difficult due to its sheer size and complex structure. Effective, automated approaches for improving the quality of SNOMED CT are needed to overcome the limitations of manual work. In this paper, we introduce a novel approach to systematically identify inconsistencies, which may manifest missing hierarchical relations and concepts in SNOMED CT, based on the structural properties of non-lattice subgraphs and the lexical properties of concepts involved in these subgraphs. A random subset of subgraphs automatically generated using this approach was reviewed by domain experts to confirm the uncovered inconsistencies. BACKGROUND AND SIGNIFICANCE
In previous work REF , we introduced a hybrid structural-lexical approach based on the lexical patterns of concept names in non-lattice subgraphs to automatically suggest missing hierarchical relations and concepts in SNOMED CT.
Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT
Journal of the American Medical Informatics Association
Mathematics Medicine Computer Science