Conceptual Complexity in Biomedical Terminologies: The UMLS Approach

McCray, Alexa T.

doi:10.1007/978-3-642-59051-1_50

Cited by 3 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is in addition a remarkable assertion to be found elsewhere in McCray (1993) that "Philosophers of language distinguish between the 'intension' and the 'extension' of a term. The intension is the manner in which a term is described, and the extension is what the term refers to in the real world."…”

Section: Concepts and Realitymentioning

confidence: 93%

“…There definitely are flaws in the treatment of concepts to be found in the Metathesaurus (and I will mention an additional one in the next section), but resting on a notion of concept that is psychologistic or subjective is not among them. And while Smith accuses the Metathesaurus Semantic Network of employing a circular definition of 'concept', this happens in a context (McCray (1993)) in which it is not a definition that is being proffered, but rather an informal explication or illustration. (I cannot in fact find the precise example from the paper that Smith cites, but there is a similar one.…”

Section: Concepts and Realitymentioning

confidence: 99%

See 1 more Smart Citation

Concepts and Synonymy in the UMLS Metathesaurus

Merrill

2009

Disc Collab

View full text Add to dashboard Cite

This paper advances a detailed exploration of the complex relationships among terms, concepts, and synonymy in the UMLS Metathesaurus, and proposes the study and understanding of the Metathesaurus from a model-theoretic perspective. Initial sections provide the background and motivation for such an approach, and a careful informal treatment of these notions is offered as a context and basis for the formal analysis. What emerges from this is a set of puzzles and confusions in the Metathesaurus and its literature pertaining to synonymy and its relation to terms and concepts. A model theory for a segment of the Metathesaurus is then constructed, and its adequacy relative to the informal treatment is demonstrated. Finally, it is shown how this approach clarifies and addresses the puzzles educed from the informal discussion, and how the model-theoretic perspective may be employed to evaluate some fundamental criticisms of the Metathesaurus.

show abstract

Section: Concepts and Realitymentioning

confidence: 93%

Section: Concepts and Realitymentioning

confidence: 99%

Concepts and Synonymy in the UMLS Metathesaurus

Merrill

2009

Disc Collab

View full text Add to dashboard Cite

show abstract

“…To increase the coverage of the resources and the potential of an automatic mapping, OMOP2OBO leverages the National Library of Medicine's UMLS (MRCONSO and MRSTY tables [2020AA version 105 ]) 70 . These data are used to annotate each OMOP concept with a UMLS CUI and a Semantic Type 71 . Additionally, the mappings provided by the MRCONSO table are used to enhance existing database cross-reference mappings provided by OMOP and the OBO Foundry ontologies (both described in detail in the "Input data used to create OMOP2OBO mappings" section).…”

Section: Omop2obo Algorithm: Resourcesmentioning

confidence: 99%

Ontologizing health systems data at scale: making translational discovery a reality

et al. 2023

View full text Add to dashboard Cite

Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68–99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

show abstract

“…Many problems involve societal and economic constraints, but there are many challenging informatics problems in producing such a record: standards of medical vocabulary and nomenclature for describing findings, conditions, and therapies, and translations across medical nomenclatures and systems nationally and internationally. The Unified Medical Language System (UMLS) has made great progress in providing a common set of linkages between existing nomenclatures and various health specialty terminologies [21,22]. Progress on standard data interchange formats are also encouraging [23], but much more needs to be done.…”

Section: Informatics Transforming Health Care In a Globalizing Societymentioning

confidence: 99%

The Micro-Macro Spectrum of Medical Informatics Challenges: From Molecular Medicine to Transforming Health Care in a Globalizing Society

Kulikowski

2002

Methods Inf Med

View full text Add to dashboard Cite

Summary Background: Medical informatics has always encompassed a very broad spectrum of techniques for clinical and biomedical research, education and practice. There has been a concomitant variety of depth of specialization, ranging from the routine application of information processing methods to cutting-edge research on fundamental problems of computer-based systems and their relations to cognition and perception in biomedicine. Objectives: Challenges for the field can be placed in perspective by considering the scale of each – from the highly detailed scientific problems in bioinformatics and emerging molecular medicine to the broad and complex social problems of introducing medical informatics into web-related global settings. Methods: The scale of an informatics problem is not only determined by the inherent physical space in which it exists, but also by the conceptual complexity that it involves, reinforcing the need to investigate the semantic web within which medical informatics is defined. Results and Conclusion: Bioinformatics, biomedical imaging and language understanding provide examples that anchor research and practice in biomedical informatics at the detailed, scientific end of the spectrum. Traditional concerns of medical informatics in the clinical arena make up the broad mid-range of the spectrum, while novel social interaction models of competition and cooperation will be needed to understand the implications of distributed health information technology for individual and societal change in an increasingly interconnected world.

show abstract

Conceptual Complexity in Biomedical Terminologies: The UMLS Approach

Cited by 3 publications

References 5 publications

Concepts and Synonymy in the UMLS Metathesaurus

Concepts and Synonymy in the UMLS Metathesaurus

Ontologizing health systems data at scale: making translational discovery a reality

The Micro-Macro Spectrum of Medical Informatics Challenges: From Molecular Medicine to Transforming Health Care in a Globalizing Society

Contact Info

Product

Resources

About