| Most existing controlled terminologies can be characterized as collections of terms, wherein the terms are arranged in a simple list or organized in a hierarchy. These kinds of terminologies are considered useful for standardizing terms and encoding data and are currently used in many existing information systems. Therefore, a large amount of data has already been recorded, and will continue to be recorded over time, using these terminologies. However, they suffer from a number of limitations that make data reuse difficult. Relatively recently, it has been proposed that formal ontological methods can be applied to some of the problems of terminological design. However, the application of these methods to existing terminologies is not straightforward. The use of these terminologies is firmly entrenched in many systems, and what might seem to be a simple option of replacing these terminologies is not possible. Moreover, these terminologies have to evolve over time in order to suit the needs of users. Any methodology must therefore take these constraints into consideration. This situation underlies the motivation for my work on exploring and evaluating methods of applying formal ontological methods to their maintenance. In this work, I have developed a formal representation of the concept-term relationship, which serves as a basis for a methodology that I have developed for the management of terminology changes. I have also implemented the methodology in a terminology maintenance tool. Then, to evaluate the methodology, I compared two methods for retrieving ICD-9-CM data, based on their recall when retrieving data for ICD-9-CM terms whose codes had changed but which had retained their original meaning. The results show that recall is either the same or better with a retrieval method that takes into account the effects of terminology. Statistically significant differences were detected (p<0.05) with the McNemar test for two terms whose codes had changed. Furthermore, when all the cases are combined in an overall category, our method 2 also performs statistically significantly better than default method 1 (p < 0.05). |
