Evidence map›Paper›PMID 18660879›Full record

ArticleYearbook of medical informatics2008

Biomedical ontologies in action: role in knowledge management, data integration and decision support.

O Bodenreider

Open access · hybridAbstract read
In one paragraph

Article in Yearbook of medical informatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 157 papers.

0numbers the graph read from it
0cells of the map it votes in
157citing papers in PubMed
7.9field-weighted citation impact, top 2% of its field
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

157 citing papers in PubMed, 369 citations in OpenAlex.

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  6. Evaluating RAG and Non-RAG Pipelines for Concept Discovery in Environmental Health Ontologies.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
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  8. Recommendations for Standardizing Nuclear Medicine Terminology and Data in the Era of Theranostics and Artificial Intelligence.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025
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  15. From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
  16. Article
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  19. A deep learning approach to identify missing is-a relations in SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2023
    Article
  20. Knowledge Graph Embeddings for ICU readmission prediction.BMC medical informatics and decision making · 2023
    Article

97 more citing papers are in PubMed but not listed here.

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

1 author at 1 institution in 1 country.

O BodenreiderNational Library of Medicine, 8600 Rockville Pike - MS 3841 (Bldg 38A, Rm B1N28U), Bethesda, MD 20894, USA. olivier@nlm.nih.gov
National Institutes of Health · US

Funding

Intramural NIH HHS Z99 LM999999
6 · The paper itself

Abstract

objectivesTo provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support.

methodsBiomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles.

resultsThe ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery).

conclusionsOntologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed.

Indexed as

Decision Support TechniquesInformation ManagementVocabulary, ControlledBibliometricsNatural Language ProcessingPubMedSemanticsTerminology as Topic

Identifiers

PMID18660879
PMCPMC2592252
OpenAlexW2156997379

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.