Evidence map›Paper›PMID 17241777›Full record

ArticleArtificial intelligence in medicine2007

Investigating subsumption in SNOMED CT: an exploration into large description logic-based biomedical terminologies.

Olivier Bodenreider, Barry Smith, Anand Kumar, Anita Burgun

Open access · greenAbstract read
In one paragraph

Article in Artificial intelligence in medicine, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 4% 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

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 89 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Search Datasets in Literature: A Case Study of GWAS.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017
    Article
  7. Article
  8. Taxonomy-Based Approaches to Quality Assurance of Ontologies.Journal of healthcare engineering · 2017
    Article
  9. Article
  10. Scalable quality assurance for large SNOMED CT hierarchies using subject-based subtaxonomies.Journal of the American Medical Informatics Association : JAMIA · 2015
    Article
  11. Review
  12. Quality assurance in LOINC using Description Logic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012
    Article
  13. OPIC: Ontology-driven Patient Information Capturing system for epilepsy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2012
    Article
  14. Article
  15. Article
  16. Identifying Granularity Differences between Large Biomedical Ontologies through Rules.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2010
    Article
  17. Article
  18. Article
  19. Article
  20. Auditing associative relations across two knowledge sources.Journal of biomedical informatics · 2009
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 3 countries.

Olivier BodenreiderU.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA. olivier@nlm.nih.gov
Barry Smith
Anand Kumar
Anita Burgun
Saarland University · DENational Institutes of Health · USUniversité de Rennes · FR

Funding

STANFORD COMPOSITE(RMI)U54HG004028 · NHGRI · STANFORD UNIVERSITY · PI MUSEN, MARK A · 2005 to 2014
$36.2M
Intramural NIH HHS Z99 LM999999NHGRI NIH HHS U54 HG004028
6 · The paper itself

Abstract

objectiveFormalisms based on one or other flavor of description logic (DL) are sometimes put forward as helping to ensure that terminologies and controlled vocabularies comply with sound ontological principles. The objective of this paper is to study the degree to which one DL-based biomedical terminology (SNOMED CT) does indeed comply with such principles. MATERIALS AND

methodsWe defined seven ontological principles (for example: each class must have at least one parent, each class must differ from its parent) and examined the properties of SNOMED CT classes with respect to these principles.

resultsOur major results are 31% of these classes have a single child; 27% have multiple parents; 51% do not exhibit any differentiae between the description of the parent and that of the child.

conclusionsThe applications of this principles to quality assurance for ontologies are discussed and suggestions are made for dealing with the phenomenon of multiple inheritance. The advantages and limitations of our approach are also discussed.

Indexed as

Computational BiologyExpert SystemsLogicNatural Language ProcessingSystematized Nomenclature of MedicineTerminology as TopicBiomedical ResearchDecision TreesHumansInformation Storage and RetrievalQuality ControlSemantics

Identifiers

PMID17241777
PMCPMC2442845
OpenAlexW2136803152

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.