Kent A. Spackman

Structural methodologies for auditing SNOMED.

SNOMED is one of the leading health care terminologies being used worldwide. As such, quality assurance is an important part of its maintenance cycle. Methodologies for auditing SNOMED based on structural aspects of its organization are presented. In particular, automated techniques for partitioning SNOMED into smaller groups of concepts based primarily on relationships patterns are defined. Two abstraction networks, the area taxonomy and p-area taxonomy, are derived from the partitions. The high-level views afforded by these abstraction networks form the basis for systematic auditing. The networks tend to highlight errors that manifest themselves as irregularities at the abstract level. They also support group-based auditing, where sets of purportedly similar concepts are focused on for review. The auditing methodologies are demonstrated on one of SNOMEDs top-level hierarchies. Errors discovered during the auditing process are reported.

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts

BackgroundText-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction.ResultsPerformance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs.ConclusionThe method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.

Replacing SEP-Triplets in SNOMED CT Using Tractable Description Logic Operators

Reification of parthood relations according to the SEP-triplet encoding pattern has been employed in the clinical terminology SNOMED CT to simulate transitivity of the part-Of relation via transitivity of the is-a relation and to inherit properties along part-Of links. In this paper we argue that using a more expressive representation language, which allows for a direct representation of the relevant properties of the part-Of relation, makes modelling less error prone while having no adverse effect on the efficiency of reasoning.

Scalable representations of diseases in biomedical ontologies

BackgroundThe 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.ResultsWe 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.ConclusionsWe 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.

Controlled terminology for clinically-relevant indexing and selective retrieval of biomedical images

Existing clinical nomenclatures do not provide comprehensive, detailed coverage for multispecialty biomedical imaging. To address clinical needs in this area, the College of American Pathologists (CAP), secretariat of the Systematized Nomenclature of Human and Veterinary Medicine (SNOMED), has entered into partnership with the DICOM (Digital Imaging and Communications in Medicine) Standards Committee, the American College of Radiology, the American Dental Association, the American Academy of Ophthalmology, the American Society for Gastrointestinal Endoscopy, the American Academy of Neurology, the American Veterinary Medical Association, and other professional specialty organizations to develop the controlled terminology that is needed for diagnostic imaging applications. Terminology development is coordinated with ongoing development and maintenance of the DICOM Standard. SNOMED content is being enhanced in two general areas: 1) imaging procedure descriptions and 2) diagnostic observations. The SNOMED DICOM Microglossary (SDM) has been developed to provide context-dependent value sets (SDM Context Groups) for DICOM codedentry data elements and semantic content specifications (SDM Templates) for reports and other structures composed of multiple data elements. The capability of storing explicitlylabeled coded descriptors from the SDM in DICOM images and reports improves the potential for selective retrieval of images and related information. A pilot test of distributed multispecialty terminology development using a World Wide Web (WWW) application was performed in 1997, demonstrating the feasibility of large-scale distributed development of SDM

Sharing ontology between ICD 11 and SNOMED CT will enable seamless re-use and semantic interoperability.

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.

Semantic Alignment between ICD-11 and SNOMED CT.

Due to fundamental differences in design and editorial policies, semantic interoperability between two de facto standard terminologies in the healthcare domain--the International Classification of Diseases (ICD) and SNOMED CT (SCT), requires combining two different approaches: (i) axiom-based, which states logically what is universally true, using an ontology language such as OWL; (ii) rule-based, expressed as queries on the axiom-based knowledge. We present the ICD-SCT harmonization process including: a) a new architecture for ICD-11, b) a protocol for the semantic alignment of ICD and SCT, and c) preliminary results of the alignment applied to more than half the domain currently covered by the draft ICD-11.

Concerning SNOMED-CT content for public health case reports

In a recent JAMIA article,1 Deepthi Rajeeve asserts that ‘some of the existing concepts [in SNOMED CT] do not meet the case definition and do not represent reportable conditions because non-human conditions are included as children in the hierarchy’, specifically citing campylobacteriosis and porcine intestinal adenomatosis. The authors suggest creating a SNOMED CT hierarchy that is exclusively human conditions. First we must point out that the term they selected (campylobacteriosis) does not mean ‘campylobacter-induced disease in human beings’. Campylobacteriosis means (in SNOMED CT and in the real world) ‘disorder characterized by infection by any campylobacter species’. No information on the species affected is or should be implied. In fact, we believe that several disorders in this hierarchy, including the cited ‘porcine enteric adenomatosis’, are likely to be overspecified by the inclusion of species. Inclusion of species in the naming of disorders is an artifact of usage patterns that does not allow for the fact that many …

Medical data standards

Existing clinical nomenclatures do not provide comprehensive, detailed coverage for multispecialty biomedical imaging. To address clinical needs in this area, the College of American Pathologists (CAP), secretariat of the Systematized Nomenclature of Human and Veterinary Medicine (SNOMED), has entered into partnership with the DICOM (Digital Imaging and Communications in Medicine) Standards Committee, the American College of Radiology, the American Dental Association, the American Academy of Ophthalmology, the American Society for Gastrointestinal Endoscopy, the American Academy of Neurology, the American Veterinary Medical Association, and other professional specialty organizations to develop the controlled terminology that is needed for diagnostic imaging applications. Terminology development is coordinated with ongoing development and maintenance of the DICOM Standard. SNOMED content is being enhanced in two general areas: 1) imaging procedure descriptions and 2) diagnostic observations. The SNOMED DICOM Microglossary (SDM) has been developed to provide context-dependent value sets (SDM Context Groups) for DICOM codedentry data elements and semantic content specifications (SDM Templates) for reports and other structures composed of multiple data elements. The capability of storing explicitlylabeled coded descriptors from the SDM in DICOM images and reports improves the potential for selective retrieval of images and related information. A pilot test of distributed multispecialty terminology development using a World Wide Web (WWW) application was performed in 1997, demonstrating the feasibility of large-scale distributed development of SDM

ICD-11 and SNOMED CT Common Ontology: Circulatory System

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.