ArticleJournal of biomedical informatics2013
Rule-based support system for multiple UMLS semantic type assignments.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients.BMC medical informatics and decision making · 2023Article
- Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata.PeerJ. Computer science · 2022Article
- Clinical Concept Extraction with Lexical Semantics to Support Automatic Annotation.International journal of environmental research and public health · 2021Article
- A review of auditing techniques for the Unified Medical Language System.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data Mapping.IEEE transactions on nanobioscience · 2018Article
- Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.Journal of biomedical informatics · 2018Review
- Validating UMLS Semantic Type Assignments Using SNOMED CT Semantic Tags.Methods of information in medicine · 2018Article
- Leveraging Horizontal Density Differences between Ontologies to Identify Missing Child Concepts: A Proof of Concept.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Auditing the Assignments of Top-Level Semantic Types in the UMLS Semantic Network to UMLS Concepts.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
- Quality Assurance of UMLS Semantic Type Assignments Using SNOMED CT Hierarchies.Methods of information in medicine · 2016Article
- Sculpting the UMLS Refined Semantic Network.Online journal of public health informatics · 2014Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundWhen new concepts are inserted into the UMLS, they are assigned one or several semantic types from the UMLS Semantic Network by the UMLS editors. However, not every combination of semantic types is permissible. It was observed that many concepts with rare combinations of semantic types have erroneous semantic type assignments or prohibited combinations of semantic types. The correction of such errors is resource-intensive.
objectiveWe design a computational system to inform UMLS editors as to whether a specific combination of two, three, four, or five semantic types is permissible or prohibited or questionable.
methodsWe identify a set of inclusion and exclusion instructions in the UMLS Semantic Network documentation and derive corresponding rule-categories as well as rule-categories from the UMLS concept content. We then design an algorithm adviseEditor based on these rule-categories. The algorithm specifies rules for an editor how to proceed when considering a tuple (pair, triple, quadruple, quintuple) of semantic types to be assigned to a concept.
resultsEight rule-categories were identified. A Web-based system was developed to implement the adviseEditor algorithm, which returns for an input combination of semantic types whether it is permitted, prohibited or (in a few cases) requires more research. The numbers of semantic type pairs assigned to each rule-category are reported. Interesting examples for each rule-category are illustrated. Cases of semantic type assignments that contradict rules are listed, including recently introduced ones.
conclusionThe adviseEditor system implements explicit and implicit knowledge available in the UMLS in a system that informs UMLS editors about the permissibility of a desired combination of semantic types. Using adviseEditor might help accelerate the work of the UMLS editors and prevent erroneous semantic type assignments.
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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.