Concept-oriented Standardization and Statistics-oriented Classification: Continuing the Classification versus Nomenclature Controversy

J. Ingenerf; W. Giere

doi:10.1055/s-0038-1634544

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1998; 37(04/05): 527-539
DOI: 10.1055/s-0038-1634544

Original Article

Schattauer GmbH

Concept-oriented Standardization and Statistics-oriented Classification: Continuing the Classification versus Nomenclature Controversy

Authors

J. Ingenerf

¹GSF – National Research Center for Environment and Health, Neuherberg
W. Giere

²J. W. Goethe – University Frankfurt/Main, Germany

Abstract

Nowadays, most activities on controlled medical vocabularies focus on the provision of a sufficient atomic-level granularity for representing clinical data. Amongst others, clinical vocabularies should be concept oriented, compositional and should also reject “Not Elsewhere Classified” [1]. We strongly share the opinion that there is a need to deal with serious deficits of existing manually created vocabularies and with new demands for computer-based advanced processing and exchange of medical language data. However, we do not share the opinion that methodological requirements like observational and structural comparability needed for sound statistics should not be included in desiderata of controlled medical vocabularies. Statistical-oriented classifications are not developed for representing detailed clinical data but for providing purposedependent classes where cases of interest are assigned uniquely. Either statistical classifications are not included into the set of controlled medical vocabularies in the sense of Cimino, or his desiderata are misleading. We argue that statistical classifications should be linked to (formal) concept systems, but again this linkage does not change their different natures. With this article we continue the “classification versus nomenclature” controversy referring to Cote [2].

Keywords

Medical Informatics - Controlled Vocabulary - Documentation - Data Interpretation/Statistical

Full Text

References

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