Fingerprinting Biomedical Terminologies--Automatic Classification and Visualization of Biomedical Vocabularies through UMLS Semantic Group Profiles.
OBJECTIVE
To explore automatic methods for the classification of biomedical vocabularies based on their content.


METHODS
We create semantic group profiles for each source vocabulary in the UMLS and compare the vectors using a Euclidian distance. We explore several techniques for visualizing individual semantic group profiles and the entire distance matrix, including donut pie charts, heatmaps, dendrograms and networks.


RESULTS
We provide donut pie charts for individual source vocavularies, as well as a heatmap, dendrogram and network for a subset of 78 vocabularies from the UMLS.


CONCLUSIONS
Our approach to fingerprinting biomedical terminologies is completely automated and can easily be applied to all source vocabularies in the UMLS, including upcoming versions of the UMLS. It supports the exploration, selection and comparison of the biomedical terminologies integrated into the UMLS. The visualizations are available at (http://mor.-nlm.nih.gov/pubs/supp/2015-medinfo-br/index.html).
AP-HP, University Hospital Georges Pompidou; INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France.
Bastien
B
Rance
Biomedical and Health Informatics, University of Washington, Seattle, WA, USA.
Thai
T
Le
National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Olivier
O
Bodenreider
D064229:Biological Ontologies; D000069550:Machine Learning; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D012660:Semantics; D017432:Unified Medical Language System / Q000145:classification*; D014584:User-Computer Interface