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.

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.

What's in a class? Lessons learnt from the ICD - SNOMED CT harmonisation.

The upcoming ICD-11 will be harmonized with SNOMED CT via a common ontological layer (CO). We provide evidence for our hypothesis that this cannot be appropriately done by simple ontology alignment, due to diverging ontological commitment between the two terminology systems. Whereas the common ontology describes clinical situations, ICD-11 linearization codes are best to be interpreted as diagnostic statements.

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.

Competing interpretations of disorder codes in SNOMED CT and ICD.

Under ontological scrutiny we have identified two competing interpretations of disorder concepts in SNOMED. Should codes be interpreted as representing pathological conditions themselves or the situations in which a patient has those conditions? This difference has significant implications for the proposed harmonization between SNOMED CT and the new ICD-11 disease classification and indeed for any systematic review of the correctness of the SNOMED CT hierarchies. Conditions themselves are distinct, whereas in any given situation a patient may have more than one condition.

Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.

Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. "Complex" concepts, which by their very nature are more difficult to model, fall neatly into this category.

Scalable representations of diseases in biomedical ontologies.

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.

Auditing SNOMED relationships using a converse abstraction network.

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.

Complexity measures to track the evolution of a SNOMED hierarchy.

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.

Auditing complex concepts in overlapping subsets of 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.

SNOMED CT as the clinical terminology for the representation of the clinical care of the newborn infant.

Clinically relevant concepts of specialized clinical domains may not yet have been represented in SNOMED CT(R). The July 2006 release was examined with CliniClue browser to determine whether 881 terms for the clinical care of the newborn infant are represented in SNOMED CT. There was complete representation for 86.

Analysis of error concentrations in SNOMED.

Two high-level abstraction networks for the knowledge content of a terminology, known respectively as the "area taxonomy" and "p-area taxonomy," have previously been defined. Both are derived automatically from partitions of the terminology's concepts. An important application of these networks is in auditing, where a number of systematic regimens have been formulated utilizing them.

Would SNOMED CT benefit from realism-based ontology evolution?

If SNOMED CT is to serve as a biomedical reference terminology, then steps must be taken to ensure comparability of information formulated using successive versions. New releases are therefore shipped with a history mechanism. We assessed the adequacy of this mechanism for its treatment of the distinction between changes occurring on the side of entities in reality and changes in our understanding thereof.

Representation of disorders of the newborn infant by SNOMED CT.

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.

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.

Rates of change in a large clinical terminology: three years experience with SNOMED Clinical Terms.

The Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) was produced by merging SNOMED Reference Terminology (RT) with Clinical Terms version 3 (CTV3). It was first released in January 2002. This paper summarizes the overall size of the terminology and its rates of change over a period of three calendar years, comprising six subsequent releases each occurring at six month intervals.

SNOMED representation of explanatory knowledge in veterinary clinical pathology.

Background: The Systematized Nomenclature of Medicine (SNOMED) is an established standard nomenclature for the expression of human and veterinary medical concepts. Nomenclature standards ease sharing of medical information, create common points of understanding, and improve data aggregation and analysis.

Objectives: The objective of this study was to determine whether SNOMED adequately represented concepts relevant to veterinary clinical pathology.

The use of SNOMED CT simplifies querying of a clinical data warehouse.

The usefulness of digital clinical information is limited by difficulty in accessing that information. Information in electronic medical records (EMR) must be entered and stored at the appropriate level of granularity for individual patient care. However, benefits such as outcomes research and decision support require aggregation to clinical data -- "heart disease" as opposed to "S/P MI 1997" for example.

The SNOMED clinical terms development process: refinement and analysis of content.

SNOMED Clinical Terms is a comprehensive concept-based health care terminology that was created by merging SNOMED RT and Clinical Terms Version 3. Following the mapping of concepts and descriptions into a merged database, the terminology was further refined by adding new content, modeling the relationships of individual concepts, and reviewing the hierarchical structure. A quality control process was performed to ensure integrity of the data.

Role grouping as an extension to the description logic of Ontylog, motivated by concept modeling in SNOMED.

Several clinical terminologies now utilize description logic to model the logical definitions of concepts. Recent editions of the Systematized Nomenclature of Medicine (SNOMED) have been developed using the description logic Ontylog. A significant design criterion for SNOMED is to keep concept expressions simple enough to be broadly usable by clinicians, while maintaining faithful representation of concept meaning.

Delivering bioinformatics training: bridging the gaps between computer science and biomedicine.

Biomedical researchers have always sought innovative methodologies to elucidate the underlying biology in their experimental models. As the pace of research has increased with new technologies that 'scale-up' these experiments, researchers have developed acute needs for the information technologies which assist them in managing and processing their experiments and results into useful data analyses that support scientific discovery. The application of information technology to support this discovery process is often called bioinformatics.

Selective retrieval of pre- and post-coordinated SNOMED concepts.

In general, it is very straightforward to store concept identifiers in electronic medical records and represent them in messages. Information models typically specify the fields that can contain coded entries. For each of these fields there may be additional constraints governing exactly which concept identifiers are applicable.