Evidence map›Paper›PMID 25422719›Full record

ArticleOnline journal of public health informatics2014

Sculpting the UMLS Refined Semantic Network.

Zhe He, C Paul Morrey, Yehoshua Perl, Gai Elhanan, Ling Chen, Yan Chen, James Geller

Abstract read
In one paragraph

Article in Online journal of public health informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

8 citing papers in PubMed.

  1. Article
  2. A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020
    Review
  3. Review
  4. Article
  5. Auditing the Assignments of Top-Level Semantic Types in the UMLS Semantic Network to UMLS Concepts.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017
    Article
  6. Article
  7. Article
  8. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Zhe HeDepartment of Biomedical Informatics, Columbia University, New York, NY, USA.
C Paul MorreyDepartment of Information Systems and Technology, Utah Valley University, Orem, UT, USA.
Yehoshua PerlDepartment of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
Gai ElhananHalfpenny Technologies, Inc., Blue Bell, PA.
Ling ChenBMCC, City University of New York, New York, NY.
Yan ChenBMCC, City University of New York, New York, NY.
James GellerDepartment of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.

Funding

Partitioning to Support Auditing and Extending the UMLSR01LM008445 · NLM · NEW JERSEY INSTITUTE OF TECHNOLOGY · PI PERL, YEHOSHUA · 2006 to 2009
$1.7M
NLM NIH HHS R01 LM008445
6 · The paper itself

Abstract

backgroundThe Refined Semantic Network (RSN) for the UMLS was previously introduced to complement the UMLS Semantic Network (SN). The RSN partitions the UMLS Metathesaurus (META) into disjoint groups of concepts. Each such group is semantically uniform. However, the RSN was initially an order of magnitude larger than the SN, which is undesirable since to be useful, a semantic network should be compact. Most semantic types in the RSN represent combinations of semantic types in the UMLS SN. Such a "combination semantic type" is called Intersection Semantic Type (IST). Many ISTs are assigned to very few concepts. Moreover, when reviewing those concepts, many semantic type assignment inconsistencies were found. After correcting those inconsistencies many ISTs, among them some that contradicted UMLS rules, disappeared, which made the RSN smaller.

objectiveThe authors performed a longitudinal study with the goal of reducing the size of the RSN to become compact. This goal was achieved by correcting inconsistencies and errors in the IST assignments in the UMLS, which additionally helped identify and correct ambiguities, inconsistencies, and errors in source terminologies widely used in the realm of public health.

methodsIn this paper, we discuss the process and steps employed in this longitudinal study and the intermediate results for different stages. The sculpting process includes removing redundant semantic type assignments, expanding semantic type assignments, and removing illegitimate ISTs by auditing ISTs of small extents. However, the emphasis of this paper is not on the auditing methodologies employed during the process, since they were introduced in earlier publications, but on the strategy of employing them in order to transform the RSN into a compact network. For this paper we also performed a comprehensive audit of 168 "small ISTs" in the 2013AA version of the UMLS to finalize the longitudinal study.

resultsOver the years it was found that the editors of the UMLS introduced some new inconsistencies that resulted in the reintroduction of unwarranted ISTs that had already been eliminated as a result of their previous corrections. Because of that, the transformation of the RSN into a compact network covering all necessary categories for the UMLS was slowed down. The corrections suggested by an audit of the 2013AA version of the UMLS achieve a compact RSN of equal magnitude as the UMLS SN. The number of ISTs has been reduced to 336. We also demonstrate how auditing the semantic type assignments of UMLS concepts can expose other modeling errors in the UMLS source terminologies, e.g., SNOMED CT, LOINC, and RxNORM that are important for health informatics. Such errors would otherwise stay hidden.

conclusionsIt is hoped that the UMLS curators will implement all required corrections and use the RSN along with the SN when maintaining and extending the UMLS. When used correctly, the RSN will support the prevention of the accidental introduction of inconsistent semantic type assignments into the UMLS. Furthermore, this way the RSN will support the exposure of other hidden errors and inconsistencies in health informatics terminologies, which are sources of the UMLS. Notably, the development of the RSN materializes the deeper, more refined Semantic Network for the UMLS that its designers envisioned originally but had not implemented.

Indexed as

Abstraction NetworkCorrection of InconsistenciesIntersection Semantic TypesRefined Semantic NetworkRefined Semantic TypesSemantic NetworkUMLS

Identifiers

PMID25422719
PMCPMC4235323

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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.