More Like this

1. Leveraging logical definitions and lexical features to detect missing IS-A relations in biomedical terminologies

   https://doi.org/10.1186/s13326-024-00309-y  

   Abeysinghe, Rashmie ; Zheng, Fengbo ; Shi, Jay ; Lhatoo, Samden D ; Cui, Licong ( May 2024 , Journal of Biomedical Semantics)

   Biomedical terminologies play a vital role in managing biomedical data. Missing IS-A relations in a biomedical terminology could be detrimental to its downstream usages. In this paper, we investigate an approach combining logical definitions and lexical features to discover missing IS-A relations in two biomedical terminologies: SNOMED CT and the National Cancer Institute (NCI) thesaurus. The method is applied to unrelated concept-pairs within non-lattice subgraphs: graph fragments within a terminology likely to contain various inconsistencies. Our approach first compares whether the logical definition of a concept is more general than  that of the other concept. Then, we check whether the lexical features of the concept are contained in those of the other concept. If both constraints are satisfied, we suggest a potentially missing IS-A relation between the two concepts. The method identified 982 potential missing IS-A relations for SNOMED CT and 100 for NCI thesaurus. In order to assess the efficacy of our approach, a random sample of results belonging to the “Clinical Findings” and “Procedure” subhierarchies of SNOMED CT and results belonging to the “Drug, Food, Chemical or Biomedical Material” subhierarchy of the NCI thesaurus were evaluated by domain experts. The evaluation results revealed that 118 out of 150 suggestions are valid for SNOMED CT and 17 out of 20 are valid for NCI thesaurus.

    

   more » « less
2. Enhancing the Quality of Hierarchic Relations in the National Cancer Institute Thesaurus to Enable Faceted Query of Cancer Registry Data

   https://doi.org/10.1200/CCI.19.00124  

   Cui, Licong ; Abeysinghe, Rashmie ; Zheng, Fengbo ; Tao, Shiqiang ; Zeng, Ningzhou ; Hands, Isaac ; Durbin, Eric B. ; Whiteman, Lori ; Remennik, Lyubov ; Sioutos, Nicholas ; et al ( May 2020 , JCO Clinical Cancer Informatics)

   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. 

   more » « less
3. A transformation-based method for auditing the IS-A hierarchy of biomedical terminologies in the Unified Medical Language System

   https://doi.org/10.1093/jamia/ocaa123  

   Zheng, Fengbo ; Shi, Jay ; Yang, Yuntao ; Zheng, W Jim ; Cui, Licong ( October 2020 , Journal of the American Medical Informatics Association)

   null (Ed.)

   Abstract Objective The Unified Medical Language System (UMLS) integrates various source terminologies to support interoperability between biomedical information systems. In this article, we introduce a novel transformation-based auditing method that leverages the UMLS knowledge to systematically identify missing hierarchical IS-A relations in the source terminologies. Materials and Methods Given a concept name in the UMLS, we first identify its base and secondary noun chunks. For each identified noun chunk, we generate replacement candidates that are more general than the noun chunk. Then, we replace the noun chunks with their replacement candidates to generate new potential concept names that may serve as supertypes of the original concept. If a newly generated name is an existing concept name in the same source terminology with the original concept, then a potentially missing IS-A relation between the original and the new concept is identified. Results Applying our transformation-based method to English-language concept names in the UMLS (2019AB release), a total of 39 359 potentially missing IS-A relations were detected in 13 source terminologies. Domain experts evaluated a random sample of 200 potentially missing IS-A relations identified in the SNOMED CT (U.S. edition) and 100 in Gene Ontology. A total of 173 of 200 and 63 of 100 potentially missing IS-A relations were confirmed by domain experts, indicating that our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively. Conclusions Our results showed that our transformation-based method is effective in identifying missing IS-A relations in the UMLS source terminologies. 

   more » « less
4. A Lexical-based Formal Concept Analysis Method to Identify Missing Concepts in the NCI Thesaurus

   https://doi.org/10.1109/BIBM49941.2020.9313186  

   Zheng, Fengbo ; Cui, Licong ( December 2020 , 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

   null (Ed.)

   Biomedical terminologies have been increasingly used in modern biomedical research and applications to facilitate data management and ensure semantic interoperability. As part of the evolution process, new concepts are regularly added to biomedical terminologies in response to the evolving domain knowledge and emerging applications. Most existing concept enrichment methods suggest new concepts via directly importing knowledge from external sources. In this paper, we introduced a lexical method based on formal concept analysis (FCA) to identify potentially missing concepts in a given terminology by leveraging its intrinsic knowledge - concept names. We first construct the FCA formal context based on the lexical features of concepts. Then we perform multistage intersection to formalize new concepts and detect potentially missing concepts. We applied our method to the Disease or Disorder sub-hierarchy in the National Cancer Institute (NCI) Thesaurus (19.08d version) and identified a total of 8,983 potentially missing concepts. As a preliminary evaluation of our method to validate the potentially missing concepts, we further checked whether they were included in any external source terminology in the Unified Medical Language System (UMLS). The result showed that 592 out of 8,937 potentially missing concepts were found in the UMLS. 

   more » « less
5. A lexical-based approach for exhaustive detection of missing hierarchical IS-A relations in SNOMED CT

   Zheng, Fengbo ; Shi, Jay ; Cui, Licong ( January 2020 , AMIA Annual Symposium proceedings)

   null (Ed.)

   Incompleteness of ontologies affects the quality of downstream ontology-based applications. In this paper, we introduce a novel lexical-based approach to automatically detect potentially missing hierarchical IS-A relations in SNOMED CT. We model each concept with an enriched set of lexical features, by leveraging words and noun phrases in the name of the concept itself and the concept's ancestors. Then we perform subset inclusion checking to suggest potentially missing IS-A relations between concepts. We applied our approach to the September 2017 release of SNOMED CT (US edition) which suggested a total of 38,615 potentially missing IS-A relations. For evaluation, a domain expert reviewed a random sample of 100 missing IS-A relations selected from the "Clinical finding" sub-hierarchy, and confirmed 90 are valid (a precision of 90%). Additional review of invalid suggestions further revealed incorrect existing IS-A relations. Our results demonstrate that systematic analysis of the enriched lexical features of concepts is an effective approach to identify potentially missing hierarchical IS-A relations in SNOMED CT. 

   more » « less