2004
@article{CaC2004,
vgclass = {refpap},
author = {Jorge E. Caviedes and James J. Cimino},
title = {Towards the development of a conceptual distance metric
for the {UMLS}},
journal = {Journal of Biomedical Informatics},
volume = {37},
number = {2},
pages = {77--85},
month = {April},
year = {2004},
url = {http://dx.doi.org/10.1016/j.jbi.2004.02.001},
abstract = {The objective of this work is to investigate the
feasibility of conceptual similarity metrics in the framework of the
Unified Medical Language System (UMLS). We have investigated an
approach based on the minimum number of parent links between concepts,
and evaluated its performance relative to human expert estimates on
three sets of concepts for three terminologies within the UMLS (i.e.,
MeSH, ICD9CM, and SNOMED). The resulting quantitative metric enables
computer-based applications that use decision thresholds and
approximate matching criteria. The proposed conceptual matching
supports problem solving and inferencing (using high-level, generic
concepts) based on readily available data (typically represented as
low-level, specific concepts). Through the identification of
semantically similar concepts, conceptual matching also enables
reasoning in the absence of exact, or even approximate, lexical
matching. Finally, conceptual matching is relevant for terminology
development and maintenance, machine learning research, decision
support system development, and data mining research in biomedical
informatics and other fields.},
}