Evidence map›Paper›PMID 38827070›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2024

An Automated Approach for Identifying Erroneous IS-A Relations in SNOMED CT.

Ran Hu, Jay Shi, Licong Cui, Rashmie Abeysinghe

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Quantitatively assessing the impact of the quality of SNOMED CT subtype hierarchy on cohort queries.Journal of the American Medical Informatics Association : JAMIA · 2025
    Article
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

4 authors.

Ran HuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
Jay ShiIntermountain Healthcare, Denver, CO.
Licong CuiMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX.
Rashmie AbeysingheDepartment of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX.

Funding

An informatics framework for SUDEP Risk Marker Identification and Risk AssessmentR01NS116287 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CUI, LICONG · 2020 to 2024
$1.7M
Biomedical Terminology Quality Assurance for Enhancing Clinical Queries over Electronic Health RecordsR01LM013335 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CUI, LICONG · 2020 to 2021
$663k
NINDS NIH HHS R01 NS116287NLM NIH HHS R01 LM013335
6 · The paper itself

Abstract

SNOMED CT is the most comprehensive clinical terminology employed worldwide and enhancing its accuracy is of utmost importance. In this work, we introduce an automated approach to identifying erroneous IS-A relations in SNOMED CT. We first extract linked concept-pairs from which we generate Term Difference Pairs (TDPs) that contain differences between the concepts. Given a TDP, if the reversed TDP also exists and the number of linked-pairs generating this TDP is less than those generating the reversed TDP, then we suggest the former linked-pairs as potentially erroneous IS-A relations. We applied this approach to the Clinical finding and Procedure subhierarchies of the 2022 March US Edition of SNOMED CT, and obtained 52 potentially erroneous IS-A relations and a candidate list of 48 linked-pairs. A domain expert confirmed 41 out of 52 (78.8%) are valid and identified 26 erroneous IS-A relations out of 48 linked-pairs demonstrating the effectiveness of the approach.

Identifiers

PMID38827070
PMCPMC11141797

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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.