ArticleMethods of information in medicine2020
Leveraging the UMLS As a Data Standard for Rare Disease Data Normalization and Harmonization.
Article in Methods of information in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- PheNormGPT: a framework for extraction and normalization of key medical findings.Database : the journal of biological databases and curation · 2024Article
- Circulating inflammatory cytokines and risk of idiopathic pulmonary fibrosis: a Mendelian randomization study.BMC pulmonary medicine · 2023Article
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
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4 authors.
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Abstract
objectiveIn this study, we aimed to evaluate the capability of the Unified Medical Language System (UMLS) as one data standard to support data normalization and harmonization of datasets that have been developed for rare diseases. Through analysis of data mappings between multiple rare disease resources and the UMLS, we propose suggested extensions of the UMLS that will enable its adoption as a global standard in rare disease.
methodsWe analyzed data mappings between the UMLS and existing datasets on over 7,000 rare diseases that were retrieved from four publicly accessible resources: Genetic And Rare Diseases Information Center (GARD), Orphanet, Online Mendelian Inheritance in Men (OMIM), and the Monarch Disease Ontology (MONDO). Two types of disease mappings were assessed, (1) curated mappings extracted from those four resources; and (2) established mappings generated by querying the rare disease-based integrative knowledge graph developed in the previous study.
resultsWe found that 100% of OMIM concepts, and over 50% of concepts from GARD, MONDO, and Orphanet were normalized by the UMLS and accurately categorized into the appropriate UMLS semantic groups. We analyzed 58,636 UMLS mappings, which resulted in 3,876 UMLS concepts across these resources. Manual evaluation of a random set of 500 UMLS mappings demonstrated a high level of accuracy (99%) of developing those mappings, which consisted of 414 mappings of synonyms (82.8%), 76 are subtypes (15.2%), and five are siblings (1%).
conclusionThe mapping results illustrated in this study that the UMLS was able to accurately represent rare disease concepts, and their associated information, such as genes and phenotypes, and can effectively be used to support data harmonization across existing resources developed on collecting rare disease data. We recommend the adoption of the UMLS as a data standard for rare disease to enable the existing rare disease datasets to support future applications in a clinical and community settings.
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