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The aim of our study was to create a graph model for the description of LOINC ® concepts. The main objective of the constructed structure is to facilitate the alignment of French local terminologies to LOINC. The process consisted of automatically incorporating the naming rules of LOINC labels, based on punctuation. We implemented these rules and applied them to the French variants of LOINC and then created attributes and concepts described with synonymous labels. When comparing the created attributes to the stated ones, the multiple mappings led to the identification of errors that must be corrected for improving the translation quality. These mappings are consecutive to semantic errors generated during the translation process. They mainly corresponded to misinterpretations of LOINC concepts and/or LOINC attributes., (©2020 AMIA - All rights reserved.)

Health professionals are faced with challenges when they have to exploit the semantics of concepts present in clinical terminologies in support of research activities. The difficulty lies in the fact that this semantics is represented not only through the labels of concepts, but also their position in the hierarchy, and, when available, their logical and textual definitions. We investigate and contrast the lexical, hierarchical, and logical representations of concepts in SNOMED CT through the example of Anemia and three other disorders. The four use cases we developed suggest that the lexical, hierarchical, and logical representations of concepts have a limited degree of overlap, but are complementary. Finally, we draw practical implications from our findings for SNOMED CT users and developers.

Linkages between animal models of diseases and human data enable the development of translational research hypotheses. The objective of this study is to investigate two approaches to integrating phenotype and clinical information. On the one hand, we develop a terminology mapping between phenotypes from the Mammalian Phenotype Ontology (MPO) and Online Mendelian Inheritance in Man (OMIM) through the Unified Medical Language System (UMLS). On the other, we associate MPO phenotypes with OMIM manifestations through annotations made to orthologous genes. 1,469 MPO concepts (22%) were mapped successfully to some disease concept in the UMLS, of which 869 were present in OMIM. Among the 16,764 distinct MGI genes associated with human orthologs, 1,968 distinct genes were associated with both MPO and OMIM annotations. The UMLS is a valuable resource for linking phenotype terms to clinical terminologies, and these mappings between terminologies can help enrich gene annotation databases and unify phenotype representation.

Auditing biomedical terminologies often results in the identification of inconsistencies and thus helps to improve their quality. In this paper, we present a method based on Semantic Web technologies for auditing biomedical terminologies and apply it to the NCI thesaurus. We stored the NCI thesaurus concepts and their properties in an RDF triple store. By querying this store, we assessed the consistency of both hierarchical and associative relations from the NCI thesaurus among themselves and with corresponding relations in the UMLS Semantic Network. We show that the consistency is better for associative relations than for hierarchical relations. Causes for inconsistency and benefits from using Semantic Web technologies for auditing purposes are discussed.

Each biomedical system has its own way of naming the pieces of information it contains, i.e., of defining its data elements (DEs). Integrating DEs facilitates the integration of biomedical resources. However, the mapping of DEs to the UMLS is ambiguous in many cases, when any correspondence is found at all. We propose to evaluate the potential contribution of a more general terminology: WordNet. Our method is based on synonyms, definitions, and structural properties of the terminologies. We applied it to a set of 474 DEs extracted from eleven biomedical sources. We show that WordNet can improve the direct mapping of DEs to UMLS when used to validate and disambiguate UMLS direct mappings. WordNet can also help identify indirect mappings of DEs to the UMLS.

Applications exploiting the hierarchical relations recorded in the Unified Medical Language System (UMLS) Metathesaurus suffer from the presence of inconsistencies in these relations. A formal approach to identifying and eliminating circular hierarchical relations has been proposed in previous work, leading to the creation of a directed acyclic Metathesaurus graph. However, this approach is at best semi-automatic and its implementation is far from trivial. A simpler, alternative approach consists in avoiding loops while traversing the Metathesaurus graph by preventing nodes from being visited twice. Our objective is to evaluate the benefit of the formal approach to eliminating cycles over a naïve approach to avoiding them. To this end, we compared the size and semantic coherence of sets of descendants obtained by both approaches. 12% of the concepts with descendants exhibit some differences. The formal approach significantly reduces the number of descendants in these cases. The benefits in terms of semantic coherence are more subtle.

Anatomy is a major organizing principle for dis-eases. In the formal definitions provided by SNOMED CT, for example, the role 'finding site' relates disorders to anatomical entities. This study investigates SNOMED CT and compares the anatomy-based classification of diseases supported by the role finding site to the anatomy-based classification of diseases provided by subsumption (is-a) relations between diseases. For each of the 3,540 anatomical entities associated with disorders,, we compared two sets of disorders: first, the set of disorders associated with any descendant of the anatomical entity under investigation (ANAT); second, the set of dis-orders corresponding to the union of the descendants of the disorders associated with the anatomical entity under investigation (TAXO). The ANAT and TAXO sets were different for 1,231 anatomical entities (35%). In 607 cases, the overlap between ANAT and TAXO was less than 50%. When a difference was found, the TAXO set was always a subset of the ANAT set. Among the 1,025,904 subsumption relations among disorders generated by the ANAT approach, 40% were not present in TAXO. This approach helps identify missing classes and taxonomic relations in existing ontologies. It can be generalized to other kinds of partitions of biomedical ontologies.