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Abstract Background The realm of pathological entities can be subdivided into pathological dispositions, pathological processes, and pathological structures. The latter are the bearer of dispositions, which can then be realized by their manifestations — pathologic processes. Despite its ontological soundness, implementing this model via purpose-oriented domain ontologies will likely require considerable effort, both in ontology construction and maintenance, which constitutes a considerable problem for SNOMED CT, presently the largest biomedical ontology. Results We describe an ontology design pattern which allows ontologists to make assertions that blur the distinctions between dispositions, processes, and structures until necessary. Based on the domain upper-level ontology BioTop, it permits ascriptions of location and participation in the definition of pathological phenomena even without an ontological commitment to a distinction between these three categories. An analysis of SNOMED CT revealed that numerous classes in the findings/disease hierarchy are ambiguous with respect to process vs. disposition. Here our proposed approach can easily be applied to create unambiguous classes. No ambiguities could be defined regarding the distinction of structure and non-structure classes, but here we have found problematic duplications. Conclusions We defend a judicious use of disjunctive, and therefore ambiguous, classes in biomedical ontologies during the process of ontology construction and in the practice of ontology application. The use of these classes is permitted to span across several top-level categories, provided it contributes to ontology simplification and supports the intended reasoning scenarios.

The improvement of semantic interoperability between data in electronic health records and aggregated data for health statistics requires efforts to carefully align the two domain terminologies ICD and SNOMED CT. Both represent a new generation of ontology-based terminologies and classifications. The proposed alignment of these two systems and, in consequence, the validity of their cross-utilisation, requires a specific resource, named Common Ontology. We present the ICD-11 SNOMED CT Common Ontology building process including: a) the principles proposed for aligning the two systems with the help of a common model of meaning, b) the design of this common ontology, and c) preliminary results of the application to the diseases of the circulatory system. [ABSTRACT FROM AUTHOR]

In order to support semantic interoperability in eHealth systems, domain terminologies need to be carefully designed. SNOMED CT and the upcoming ICD-11 represent a new generation of ontology-based terminologies and classifications. The proposed alignment of these two systems and, in consequence, the validity of their cross-utilisation requires a thorough analysis of the intended meaning of their representational units. We present the ICD11 SNOMED CT harmonization process including: a) the clarification of the interpretation of codes in both systems as representing situations rather than conditions, b) the principles proposed for aligning the two systems with the help of a common ontology, c) the high level design of this common ontology, and d) further ontology-driven issues that have arisen in the course of this work. [ABSTRACT FROM AUTHOR]

SNOMED CT® is the most sophisticated reference terminology currently available for the representation of healthcare. An unforeseen consequence of the opportunistic evolutionary process for SNOMED CT may be that some terms for disorders of specialised clinical domains are not represented within the terminology. The SNOMED CT July 2006 release was systematically examined using the CliniClue® terminology browser to determine whether 434 terms for disorders of the newborn infant are represented within the terminology. There was complete representation for 90.8% of the terms for disorders of the newborn infant, partial representation for 6.4% of the terms, and no representation for 2.8% of the terms. Complete representation is achieved with a single, precoordinated SNOMED expression for 96.2% of the terms for disorders of the newborn infant that have complete representation within SNOMED CT. Nearly ninety percent of the SNOMED CT concepts that completely represent these terms have the current Concept Status but less than 40% of these concepts are fully defined SNOMED concepts. Nearly 50% of these SNOMED CT concepts have one or more synonyms. SNOMED CT® provides structured representation for the majority of this set of terms that are used for disorders of the newborn infant. [ABSTRACT FROM AUTHOR]

Objective To support clinically relevant indexing of biomedical images and image-related information based on the attributes of image acquisition procedures and the judgments (observations) expressed by observers in the process of image interpretation. Design The authors introduce the notion of “image acquisition context,” the set of attributes that describe image acquisition procedures, and present a standards-based strategy for utilizing the attributes of image acquisition context as indexing and retrieval keys for digital image libraries. Methods The authors' indexing strategy is based on an interdependent message/terminology architecture that combines the Digital Imaging and Communication in Medicine (DICOM) standard, the SNOMED (Systematized Nomenclature of Human and Veterinary Medicine) vocabulary, and the SNOMED DICOM microglossary. The SNOMED DICOM microglossary provides context-dependent mapping of terminology to DICOM data elements. Results The capability of embedding standard coded descriptors in DICOM image headers and image-interpretation reports improves the potential for selective retrieval of image-related information. This favorably affects information management in digital libraries. [ABSTRACT FROM PUBLISHER]

The authors describe a framework, based on the Ogden-Richards semiotic triangle, for understanding the relationship between the Unified Medical Language System (UMLS) and the source terminologies from which the UMLS derives its content. They pay particular attention to UMLS's Concept Unique Identifier (CUI) and the sense of “meaning” it represents as contrasted with the sense of “meaning” represented by the source terminologies. The CUI takes on emergent meaning through linkage to terms in different terminology systems. In some cases, a CUI's emergent meaning can differ significantly from the original sources' intended meanings of terms linked by that CUI. Identification of these different senses of meaning within the UMLS is consistent with historical themes of semantic interpretation of language. Examination of the UMLS within such a historical framework makes it possible to better understand the strengths and limitations of the UMLS approach for integrating disparate terminologic systems and to provide a model, or theoretic foundation, for evaluating the UMLS as a Possible World—that is, as a mathematical formalism that represents propositions about some perspective or interpretation of the physical world. [ABSTRACT FROM PUBLISHER]

Objective To evaluate a “lexically assign, logically refine” (LALR) strategy for merging overlapping healthcare terminologies. This strategy combines description logic classification with lexical techniques that propose initial term definitions. The lexically suggested initial definitions are manually refined by domain experts to yield description logic definitions for each term in the overlapping terminologies of interest. Logic-based techniques are then used to merge defined terms. Methods A LALR strategy was applied to 7,763 LOINC and 2,050 SNOMED procedure terms using a common set of defining relationships taken from the LOINC data model. Candidate value restrictions were derived by lexically comparing the procedure's name with other terms contained in the reference SNOMED topography, living organism, function, and chemical axes. These candidate restrictions were reviewed by a domain expert, transformed into terminologic definitions for each of the terms, and then algorithmically classified. Results The authors successfully defined 5,724 (73%) LOINC and 1,151 (56%) SNOMED procedure terms using a LALR strategy. Algorithmic classification of the defined concepts resulted in an organization mirroring that of the reference hierarchies. The classification techniques appropriately placed more detailed LOINC terms underneath the corresponding SNOMED terms, thus forming a complementary relationship between the LOINC and SNOMED terms. Discussion LALR is a successful strategy for merging overlapping terminologies in a test case where both terminologies can be defined using the same defining relationships, and where value restrictions can be drawn from a single reference hierarchy. Those concepts not having lexically suggested value restrictions frequently indicate gaps in the reference hierarchy. [ABSTRACT FROM PUBLISHER]

In SNOMED CT, a given kind of attribute relationship is defined between two hierarchies, a source and a target. Certain hierarchies (or subhierarchies) serve only as targets, with no outgoing relationships of their own. However, converse relationships-those pointing in a direction opposite to the defined relationships-while not explicitly represented in SNOMED's inferred view, can be utilized in forming an alternative view of a source. In particular, they can help shed light on a source hierarchy's overall relationship structure. Toward this end, an abstraction network, called the converse abstraction network (CAN), derived automatically from a given SNOMED hierarchy is presented. An auditing methodology based on the CAN is formulated. The methodology is applied to SNOMED's Device subhierarchy and the related device relationships of the Procedure hierarchy. The results indicate that the CAN is useful in finding opportunities for refining and improving SNOMED.

Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries.

SNOMED CT is an extensive terminology with an attendant amount of complexity. Two measures are proposed for quantifying that complexity. Both are based on abstraction networks, called the area taxonomy and the partial-area taxonomy, that provide, for example, distributions of the relationships within a SNOMED hierarchy. The complexity measures are employed specifically to track the complexity of versions of the Specimen hierarchy of SNOMED before and after it is put through an auditing process. The pre-audit and post-audit versions are compared. The results show that the auditing process indeed leads to a simplification of the terminology's structure.

SNOMED CT is the most sophisticated reference terminology currently available for the representation of healthcare. An unforeseen consequence of the opportunistic evolutionary process for SNOMED CT may be that some terms for disorders of specialised clinical domains are not represented within the terminology. The SNOMED CT July 2006 release was systematically examined using the CliniClue terminology browser to determine whether 434 terms for disorders of the newborn infant are represented within the terminology. There was complete representation for 90.8% of the terms for disorders of the newborn infant, partial representation for 6.4% of the terms, and no representation for 2.8% of the terms. Complete representation is achieved with a single, pre-coordinated SNOMED expression for 96.2% of the terms for disorders of the newborn infant that have complete representation within SNOMED CT. Nearly ninety percent of the SNOMED CT concepts that completely represent these terms have the current Concept Status but less than 40% of these concepts are fully defined SNOMED concepts. Nearly 50% of these SNOMED CT concepts have one or more synonyms. SNOMED CT provides structured representation for the majority of this set of terms that are used for disorders of the newborn infant.