Fingerprinting Biomedical Terminologies--Automatic Classification and Visualization of Biomedical Vocabularies through UMLS Semantic Group Profiles.

Bastien Rance, Thai Le, Olivier Bodenreider

Abstract read

In one paragraph

Article in Studies in health technology and informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bastien RanceAP-HP, University Hospital Georges Pompidou; INSERM, UMR_S 1138, Centre de Recherche des Cordeliers, Paris, France.

Thai LeBiomedical and Health Informatics, University of Washington, Seattle, WA, USA.

Olivier BodenreiderNational Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

Funding

University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026

$19.6M

Intramural NIH HHS Z99 LM999999NLM NIH HHS T15 LM007442

6 · The paper itself

Abstract

objectivesTo explore automatic methods for the classification of biomedical vocabularies based on their content.

methodsWe 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.

resultsWe 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.

conclusionsOur 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).

Indexed as

Biological OntologiesMachine LearningNatural Language ProcessingSemanticsUser-Computer InterfacePattern Recognition, AutomatedUnified Medical Language System

Identifiers

PMID26262156

PMCPMC5881385

What OpenQuestion holds

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LicenceCC BY-NC

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.