Affiliations: Biological Information Research Center, National
Institute of Advanced Industrial Science and Technology (AIST). E-mail:
[email protected] | National Institute of Genetics. E-mail:
[email protected], [email protected] | CREST, JST (Japan Science and Technology Corporation).
E-mail: [email protected]
Note: [] Corresponding author
Abstract: As a first step toward the quantitative comparison of clinical
features of diseases, we indexed the text descriptions in the Clinical Synopsis
section of the Online Mendelian Inheritance in Man (OMIM) with concepts for the
body parts, organs, and tissues contained in the Metathesaurus of the Unified
Medical Language System (UMLS). We also indexed the text with the diseases and
disorders having links to body parts specified in the thesaurus. The vocabulary
size was approximately 177,540 representations for 81,435 concepts, and 2,161
concepts were indexed to 3,779 OMIM entries. The indexed concepts included 134
concepts for the noun forms of anatomical concepts and 985 indexed concepts for
diseases and disorders that were linked to 132 and 408 anatomical concepts,
respectively. We report herein that the retrieval of OMIM entries for diseases
affecting specific organs can be made more comprehensive through the anatomical
concepts indexed to the Clinical Synopsis or linked to the indexed concepts, as
compared to simply matching organ names to the Clinical Synopsis text. The
recall and precision of identifying relevant body parts in the Clinical
Synopsis were calculated as 78% and 92.5%, respectively, based on random
sampling. The examination of the unidentified body parts due to lack of indexed
diseases and disorders showed that although most of the concepts for diseases
and disorders were contained in the Metathesaurus, their relations to body
parts were not. The indexing result proved the effectiveness of the
Metathesaurus as a resource for the identification of concepts indicating body
parts, diseases, and disorders.
Keywords: text mining, automated indexing, OMIM, UMLS
