Expression of a domain ontology model in unified modeling language for the World Health Organization International classification of impairment, disability, and handicap, version 2.

The International Classification of Impairment, Disability, and Handicap Version 2(ICIDH-2), an anticipated addition to the World Health Organization suite of terminologies, has been put forth as a means for standardized representation of generic health and/or functional status data. In an attempt to make explicit the ontology upon which ICIDH-2 is based the authors derived a concept model expressed as a Unified Modeling Language static class diagram through abstraction of concept-terms in the documentation provided with the Full Version Pre-Final Draft of ICIDH-2 (December 2000). ICIDH-2's semantic structure is analyzed and evaluated for its semantic consistency. Discussion is presented on the utility of domain ontology models in terminology development and potential roles ICIDH-2 might play, as it undergoes refinement towards a representational standard. It is intended that the proposed UML rendering will stimulate domain discourse and consensus that will lead to enhancement of conceptual clarity in the ICIDH-2 ontological hierarchy and further enable its study and development as a healthcare classification.

A formal approach to integrating synonyms with a reference terminology.

Medical terminologies continue to grow in scope, completeness and detail. The emerging generation of terminology systems define concepts in terms of their position within a categorical structure. It is still necessary, however, to access and represent the concepts using everyday spoken and written language, which introduces both lexical and semantic ambiguity. This ambiguity can have a negative impact on both selectivity and recall when it comes to associating free-form textual phrases with their coded equivalent. Lexical ambiguity issues can often be addressed algorithmically, but semantic ambiguity presents a more difficult problem. A common solution to the semantic problem is to associate many different representational permutations with a given target concept. This approach has several drawbacks. An alternate solution is to build separate synonym tables that can serve as permuted indices into the terms representing the underlying concepts. A potential shortcoming of this approach, however, is a further reduction in the lookup selectivity. One possible source of loss of selectivity could be "meaning drift"--the gradual change in meaning that can be introduced when following a chain of nearly synonymous words. We posited that organizing synonyms into separate "meaning clusters" might reduce this loss in precision, but the results of this study did not bear that out.

Embedded structures and representation of nursing knowledge.

Nursing Vocabulary Summit participants were challenged to consider whether reference terminology and information models might be a way to move toward better capture of data in electronic medical records. A requirement of such reference models is fidelity to representations of domain knowledge. This article discusses embedded structures in three different approaches to organizing domain knowledge: scientific reasoning, expertise, and standardized nursing languages. The concept of pressure ulcer is presented as an example of the various ways lexical elements used in relation to a specific concept are organized across systems. Different approaches to structuring information-the clinical information system, minimum data sets, and standardized messaging formats-are similarly discussed. Recommendations include identification of the polyhierarchies and categorical structures required within a reference terminology, systematic evaluations of the extent to which structured information accurately and completely represents domain knowledge, and modifications or extensions to existing multidisciplinary efforts.

Linking a medical vocabulary to a clinical data model using Abstract Syntax Notation 1.

We have created a clinical data model using Abstract Syntax Notation 1 (ASN. 1). The clinical model is constructed from a small number of simple data types that are built into data structures of progressively greater complexity. Important intermediate types include Attributes, Observations, and Events. The highest level elements in the model are messages that are used for inter-process communication within a clinical information system. Vocabulary is incorporated into the model using BaseCoded, a primitive data type that allows vocabulary concepts and semantic relationships to be referenced using standard ASN. 1 notation. ASN. 1 subtyping language was useful in preventing unbounded proliferation of object classes in the model, and in general, ASN.1 was found to be a flexible and robust notation for representing a model of clinical information.