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SNOMED CT Normal Forms

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Revision as of 19:29, 18 July 2012 by Rhausam (talk | contribs) (Created page with "<div class="subSubSection"> <div class="header"> 4.1 </div> <div class="body"> The text below is taken from the introduction to the document 'SNOMED CT Transformations to N...")
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The text below is taken from the introduction to the document 'SNOMED CT Transformations to Normal Forms', and outlines the purpose of transformations and the general method of transformation. This document, and its companion ' SNOMED CT Abstract Models and Representational Forms' can be found at [[1]]. From the perspective of integration of SNOMED CT expressions in HL7 communications the assumption is that in most cases a "close to user" form will be stored and communicated. The normal form transformation provides a method that enables consistent comparison of these expressions with one another and with retrieval queries.

The purpose of generating normal forms is to facilitate complete and accurate retrieval of pre and post-coordinated SNOMED CT expressions from clinical records or other resources.

The approach described is based on the description logic definitions of SNOMED CT concepts which are used when recording clinical statements in an electronic records system. Using this approach, expressions that are authored, stored and/or communicated in a relatively informal close-to-user form are logically transformed into a common normalized form. In this normalized form it is possible to apply simple rules to test subsumption between expressions.

The simplest case of a valid close-to-user expression is a single conceptId, and the approach described can be applied to these simple pre-coordinated expressions, as well as to more complex expressions that include multiple conceptIds and refinements (qualifiers).

Likewise, transformations and normalisations can be both simple and complex, however the general principle is that the normalisation process will restate a SNOMED CT expression in terms of the 'primitive' Concepts with which it is associated in the reference data. By example, the SNOMED CT Concept [ 80146002 | appendectomy ] would, in essence, transform under normalisation to [ 71388002 | procedure |: { 260686004 | method |= 129304002 | excision - action |, 405813007 | procedure site - Direct |= 66754008 | appendix structure | } ] ("a procedure that consists of excising an appendix").

The approach to normalization may be applied to the specific SNOMED CT expressions but may also be extended to take account of contextual information derived from the information model in which the expression is situated. Therefore, the normal form may include SNOMED CT context information, even if this is not present in the initial SNOMED CT expression. As such the result of transformation of [ 80146002 | appendectomy ] is a simplification (the additional contextual/situation information is missing), but it is hoped that the example sufficiently illustrates the principle of normalisation.

The algorithm extends earlier work on canonical forms as follow:

  • Normalizes fully-defined values within definitions or expressions producing nested expressions that are fully normalized.
  • Merges refinements stated in an expression with definitional relationships present in the definitions of the concepts referenced by the expression.
  • The merge process takes account of refinements that may not be grouped or nested in a manner that precisely reflects the structure of a current (or future) concept definition.
  • This avoids the need to add, store and communicate potentially spurious detail from current definitions to the expression recorded by a user or software application.
  • Takes account of context rules including soft default context and a preliminary approach to moodCode mapping and handling of procedures with values (present in algorithm but not yet easily visible in test environment).