Abstract not found

Abstract not found

A common language, or terminology, for representing what clinicians have said and done is an important requirement for individual clinical systems, and it is a prerequisite for integrating disparate applications in a distributed telematic healthcare environment. Formal representations based on description logics or closely related formalisms are increasingly used for representing medical terminologies. GALEN's experience in using one such formalism raises two major issues: . How to make ontologies based on description logics easy to use and understand for both clinicians and applications developers; . What features are required of the ontology and description logic if they are to achieve their aims. Based on our experience we put forward four contentions: two relating to each of these two issues: . That natural language generation is essential to make a description logic based ontology accessible to users; . That the description logic based ontology should be treated as an `assembl...

(OWL-DL) explicitly exclude defaults and exceptions, as do all logic based formalisms for ontologies. However, many biomedical applications appear to require default reasoning, at least if they are to be engineered in a maintainable way. Default reasoning has always been one of the great strengths of Frame systems such as Protégé. Resolving this conflict requires analysis of the different uses for defaults and exceptions. In some cases, alternatives can be provided within the OWL framework; in others, it appears that hybrid reasoning about a knowledge base of contingent facts built around the core ontology is necessary. Trade-offs include both human factors and the scaling of computational performance. The analysis presented here is based on the OpenGALEN experience with large scale ontologies using a formalism, GRAIL, which explicitly incorporates constructs for hybrid reasoning, numerous experiments with OWL, and initial work on combining OWL and Protégé. 1

this paper is that, therefore, clinical terminologies are now software and that this fact has profound implications for how they are designed, compiled and maintained

Controlled medical terminologies (CMTs) are playing central roles in clinical information systems and medical knowledge resource applications. As these terminologies grow, they are able to support more complex tasks but require more intensive efforts to create and maintain them. Several terminologies are evolving into knowledge bases of medical concepts. The knowledge they include is being used to support distributed cognition in two forms: complex medical decisions involving multiple people and applications, and coordination of maintenance

The theory of conceptual structures serves as a common basis for natural language processing and medical concept representation. We present a PROLOG-based formalization of dependency grammar that can accommodate conceptual structures in its dependency rules. First results indicate that this formalization provides an operational basis for the implementation of medical language parsers and for the design of medical concept representation languages.

The symbolic representation of the physical structure of living organisms needs an ontologically well-founded and logically sound approach so that formal reasoning can adequately be supported. We describe a set of canonical relations and attributes necessary for the description of biological structures. Based on these epistemological primitives, we sketch how a broad range of organisms can be represented by cascading theories which are ordered by various dimensions, such as granularity, development, species and canonicity. We thus aim at a rational reconstruction and nonredundant representation of biological structure notions.

Objective: To evaluate the utility of SNOMED RT © in support of a natural language interface for encoding of clinical assessments. Method: Using a random sample of clinical terms from the UNMC Lexicon©, I mapped the terminology into canonical data entries using SNOMED RT. Working from the source term language, I evaluated lexical mapping to the SNOMED term set, and the function of the SNOMED RT semantic network in support of a language-based clinical coding interface. Results: Ambiguity in the source terms was low at 0.3%. Lexical (language-based) mapping could account for only 48.8 % of meaning from the source terms. The RT semantic network accounted for 39.5 % of meaning, and supplementing the lexical map this led to 80.2 % capture of source content. Error rates in the segment of RT which I reviewed were low at 0.6%. 97.6 % of source content could be accurately captured in SNOMED RT. Conclusion: SNOMED RT supported an accurate and reliable representation of clinical assessment data in this sample. The semantic network of RT substantially enhanced the encoding of concepts relative to lexical mapping. However these data suggest that natural language encoding with SNOMED RT in an enterprise environment is unlikely at this time.