Ronald Cornet1, Ameen Abu-Hanna. 1. Academic Medical Center, Universiteit van Amsterdam, Department of Medical Informatics, PO Box 22700, 1100 DE Amsterdam, The Netherlands. r.cornet@amc.uva.nl
Abstract
OBJECTIVE: To specify and evaluate a method for auditing medical terminological systems (TSs) based on detecting concepts with equivalent definitions. This method addresses two important problems: redundancy, where the same concept is represented more than once (described by different terms), and underspecification, where different concepts have the same representation and hence appear indistinguishable from each other. DESIGN: The auditing method is applicable for TSs that are or can be represented in a description logic (DL). The method relies on the assumption that concept definitions are non-primitive (i.e. they are regarded as providing necessary and sufficient conditions). Whereas this assumption may not hold for many definitions, it does serve the purpose of detecting sets of logically equivalent concepts by a DL reasoner. Such a set may include the same concept which is defined more than once and/or different concepts that are underspecified as they appear indistinguishable from each other by their represented properties. Analysis of these sets provides insight into the representation quality of concepts and provides hints at improving the TS. MEASUREMENTS: In our case study the method is applied to the DICE TS, a comprehensive TS in intensive care. It comprises about 2500 concepts and 40 properties and relations. RESULTS: In DICE we found four concepts that were defined twice. Furthermore, 100 sets were found containing more than 300 underspecified concepts. The sizes of these sets ranged from 2 to 13. Analysis revealed that many concepts can be more completely defined, either by adding existing relations, or by the introduction of new relations into the terminological system. CONCLUSION: The method proved both usable and valuable for auditing TSs. DL reasoning is fully automated and all equivalent concept definitions are systematically found. The resulting sets of equivalent concepts clearly point out which concept definitions are to be reviewed, as they contain duplicate definitions of a concept, and (inherently or unnecessarily) underspecified concepts.
OBJECTIVE: To specify and evaluate a method for auditing medical terminological systems (TSs) based on detecting concepts with equivalent definitions. This method addresses two important problems: redundancy, where the same concept is represented more than once (described by different terms), and underspecification, where different concepts have the same representation and hence appear indistinguishable from each other. DESIGN: The auditing method is applicable for TSs that are or can be represented in a description logic (DL). The method relies on the assumption that concept definitions are non-primitive (i.e. they are regarded as providing necessary and sufficient conditions). Whereas this assumption may not hold for many definitions, it does serve the purpose of detecting sets of logically equivalent concepts by a DL reasoner. Such a set may include the same concept which is defined more than once and/or different concepts that are underspecified as they appear indistinguishable from each other by their represented properties. Analysis of these sets provides insight into the representation quality of concepts and provides hints at improving the TS. MEASUREMENTS: In our case study the method is applied to the DICE TS, a comprehensive TS in intensive care. It comprises about 2500 concepts and 40 properties and relations. RESULTS: In DICE we found four concepts that were defined twice. Furthermore, 100 sets were found containing more than 300 underspecified concepts. The sizes of these sets ranged from 2 to 13. Analysis revealed that many concepts can be more completely defined, either by adding existing relations, or by the introduction of new relations into the terminological system. CONCLUSION: The method proved both usable and valuable for auditing TSs. DL reasoning is fully automated and all equivalent concept definitions are systematically found. The resulting sets of equivalent concepts clearly point out which concept definitions are to be reviewed, as they contain duplicate definitions of a concept, and (inherently or unnecessarily) underspecified concepts.
Authors: Yue Wang; Duo Wei; Junchuan Xu; Gai Elhanan; Yehoshua Perl; Michael Halper; Yan Chen; Kent A Spackman; George Hripcsak Journal: AMIA Annu Symp Proc Date: 2008-11-06