ReviewJournal of biomedical informatics2012
Auditing complex concepts of SNOMED using a refined hierarchical abstraction network.
Review in Journal of biomedical informatics, 2012. 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.
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.
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.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Open versus arthroscopic ankle arthrodesis: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2020Pooled it
- Implementation of Pharmacogenomics and Artificial Intelligence Tools for Chronic Disease Management in Primary Care Setting.Journal of personalized medicine · 2021Article
- How to design a registry for undiagnosed patients in the framework of rare disease diagnosis: suggestions on software, data set and coding system.Orphanet journal of rare diseases · 2021Article
- Missing lateral relationships in top-level concepts of an ontology.BMC medical informatics and decision making · 2020Article
- Outlier concepts auditing methodology for a large family of biomedical ontologies.BMC medical informatics and decision making · 2020Article
- Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies.Journal of biomedical informatics · 2018Article
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs.Journal of biomedical informatics · 2018Article
- Overlapping Complex Concepts Have More Commission Errors, Especially in Intensive Terminology Auditing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Quality assurance of chemical ingredient classification for the National Drug File - Reference Terminology.Journal of biomedical informatics · 2017Article
- Mining non-lattice subgraphs for detecting missing hierarchical relations and concepts in SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- From SNOMED CT to Uberon: Transferability of evaluation methodology between similarly structured ontologies.Artificial intelligence in medicine · 2017Article
- Analyzing structural changes in SNOMED CT's Bacterial infectious diseases using a visual semantic delta.Journal of biomedical informatics · 2017Article
- Introducing the Big Knowledge to Use (BK2U) challenge.Annals of the New York Academy of Sciences · 2017Review
- Taxonomy-Based Approaches to Quality Assurance of Ontologies.Journal of healthcare engineering · 2017Article
- A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.Journal of biomedical informatics · 2016Article
- Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.Journal of biomedical informatics · 2016Article
- Tracking the Remodeling of SNOMED CT's Bacterial Infectious Diseases.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Article
- Structural measures to track the evolution of SNOMED CT hierarchies.Journal of biomedical informatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Auditors of a large terminology, such as SNOMED CT, face a daunting challenge. To aid them in their efforts, it is essential to devise techniques that can automatically identify concepts warranting special attention. "Complex" concepts, which by their very nature are more difficult to model, fall neatly into this category. A special kind of grouping, called a partial-area, is utilized in the characterization of complex concepts. In particular, the complex concepts that are the focus of this work are those appearing in intersections of multiple partial-areas and are thus referred to as overlapping concepts. In a companion paper, an automatic methodology for identifying and partitioning the entire collection of overlapping concepts into disjoint, singly-rooted groups, that are more manageable to work with and comprehend, has been presented. The partitioning methodology formed the foundation for the development of an abstraction network for the overlapping concepts called a disjoint partial-area taxonomy. This new disjoint partial-area taxonomy offers a collection of semantically uniform partial-areas and is exploited herein as the basis for a novel auditing methodology. The review of the overlapping concepts is done in a top-down order within semantically uniform groups. These groups are themselves reviewed in a top-down order, which proceeds from the less complex to the more complex overlapping concepts. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. Hypotheses regarding error ratios for overlapping concepts and between different kinds of overlapping concepts are formulated. Two phases of auditing the Specimen hierarchy for two releases of SNOMED are reported on. With the use of the double bootstrap and Fisher's exact test (two-tailed), the auditing of concepts and especially roots of overlapping partial-areas is shown to yield a statistically significant higher proportion of errors.
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Registered trials
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.