Clinical Terminologies in the NHS: SNOMED CT and dm+d

Clinical terminologies are fundamental to the successful recording of clinical data within Electronic Health Record systems and are important in enabling the sharing of data between systems. An overview of the structure and benefits of the international terminology SNOMED CT and the NHS dictionary of medicines and devices (dm+d) are described. Also discussed is a history of clinical terminologies used within the NHS

Ontology as the core discipline of biomedical informatics: Legacies of the past and recommendations for the future direction of research

The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In the naming of genes, proteins, and other molecular structures, considerable efforts are under way to reduce the effects of the different naming conventions which have been spawned by different groups of researchers. Electronic patient records, too, increasingly involve the use of standardized terminologies, and tremendous efforts are currently being devoted to the creation of terminology resources that can meet the needs of a future era of personalized medicine, in which genomic and clinical data can be aligned in such a way that the corresponding information systems become interoperable

New desiderata for biomedical terminologies

It is only by fixing on agreed meanings of terms in biomedical terminologies that we will be in a position to achieve that accumulation and integration of knowledge that is indispensable to progress at the frontiers of biomedicine. Standardly, the goal of fixing meanings is seen as being realized through the alignment of terms on what are called ‘concepts’. Part I addresses three versions of the concept-based approach – by Cimino, by Wüster, and by Campbell and associates – and surveys some of the problems to which they give rise, all of which have to do with a failure to anchor the terms in terminologies to corresponding referents in reality. Part II outlines a new, realist solution to this anchorage problem, which sees terminology construction as being motivated by the goal of alignment not on concepts but on the universals (kinds, types) in reality and thereby also on the corresponding instances (individuals, tokens). We outline the realist approach, and show how on its basis we can provide a benchmark of correctness for terminologies which will at the same time allow a new type of integration of terminologies and electronic health records. We conclude by outlining ways in which the framework thus defined might be exploited for purposes of diagnostic decision-support

A semi-automatic semantic method for mapping SNOMED CT concepts to VCM Icons

VCM (Visualization of Concept in Medicine) is an iconic language for representing key medical concepts by icons. However, the use of this language with reference terminologies, such as SNOMED CT, will require the mapping of its icons to the terms of these terminologies. Here, we present and evaluate a semi-automatic semantic method for the mapping of SNOMED CT concepts to VCM icons. Both SNOMED CT and VCM are compositional in nature; SNOMED CT is expressed in description logic and VCM semantics are formalized in an OWL ontology. The proposed method involves the manual mapping of a limited number of underlying concepts from the VCM ontology, followed by automatic generation of the rest of the mapping. We applied this method to the clinical findings of the SNOMED CT CORE subset, and 100 randomly-selected mappings were evaluated by three experts. The results obtained were promising, with 82 of the SNOMED CT concepts correctly linked to VCM icons according to the experts. Most of the errors were easy to fix

Desiderata for an ontology of diseases for the annotation of biological datasets.

There is a plethora of disease ontologies available, all potentially useful for the annotation of biological datasets. We define seven desirable features for such ontologies and examine whether or not these features are supported by eleven disease ontologies. The four ontologies most closely aligned with our desiderata are Disease Ontology, SNOMED CT, NCI thesaurus and UMLS