Evidence map›Paper›PMID 29854262›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017

Can SNOMED CT Changes Be Used as a Surrogate Standard for Evaluating the Performance of Its Auditing Methods?

Zhang Guo-Qiang, Huang Yan, Cui Licong

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Leveraging non-lattice subgraphs for suggestion of new concepts for SNOMED CT.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2021
    Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Zhang Guo-QiangInstitute for Biomedical Informatics, University of Kentucky, Lexington, KY.
Huang YanInstitute for Biomedical Informatics, University of Kentucky, Lexington, KY.
Cui LicongDepartment of Computer Science, University of Kentucky, Lexington, KY.

Funding

Kentucky Center for Clinical and Translational ScienceUL1TR001998 · NCATS · UNIVERSITY OF KENTUCKY · PI HARTMANN, KATHERINE E, KERN, PHILIP A · 2016 to 2025
$34.2M
NCATS NIH HHS UL1 TR001998
6 · The paper itself

Abstract

We introduce RGT, Retrospective Ground-Truthing, as a surrogate reference standard for evaluating the performance of automated Ontology Quality Assurance (OQA) methods. The key idea of RGT is to use cumulative SNOMED CT changes derived from its regular longitudinal distributions by the official SNOMED CT editorial board as a partial, surrogate reference standard. The contributions of this paper are twofold: (1) to construct an RGT reference set for SNOMED CT relational changes; and (2) to perform a comparative evaluation of the performances of lattice, non-lattice, and randomized relational error detection methods using the standard precision, recall, and geometric measures. An RGT relational-change reference set of 32,241 IS-A changes were constructed from 5 U.S. editions of SNOMED CT from September 2014 to September 2016, with reversals and changes due to deletion or addition of new concepts excluded. 68,849 independent non-lattice fragments, 118,587 independent lattice fragments, and 446,603 relations were extracted from the SNOMED CT March 2014 distribution. Comparative performance analysis of smaller (less than 15) lattice vs. non-lattice fragments was also given to approach the more realistic setting in which such methods may be applied. Among the 32,241 IS-A changes, independent non-lattice fragments covered 52.8% changes with 26.4% precision with a G-score of 0.373. Even though this G-score is significantly lower in comparison to those in information retrieval, it breaks new ground in that such evaluations have never performed before in the highly discovery-oriented setting of OQA.

Indexed as

Systematized Nomenclature of MedicineBiological OntologiesData MiningFeasibility StudiesHumansQuality Assurance, Health CareReference StandardsRetrospective Studies

Identifiers

PMID29854262
PMCPMC5977667

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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.