UMLS users and uses: a current overview

Abstract

The US National Library of Medicine regularly collects summary data on direct use of Unified Medical Language System (UMLS) resources. The summary data sources include UMLS user registration data, required annual reports submitted by registered users, and statistics on downloads and application programming interface calls. In 2019, the National Library of Medicine analyzed the summary data on 2018 UMLS use. The library also conducted a scoping review of the literature to provide additional intelligence about the research uses of UMLS as input to a planned 2020 review of UMLS production methods and priorities. 5043 direct users of UMLS data and tools downloaded 4402 copies of the UMLS resources and issued 66 130 951 UMLS application programming interface requests in 2018. The annual reports and the scoping review results agree that the primary UMLS uses are to process and interpret text and facilitate mapping or linking between terminologies. These uses align with the original stated purpose of the UMLS.

Pragmatic MDR: a metadata repository with bottom-up standardization of medical metadata through reuse

“…In prior work, the metadata of SHIP was already converted to ODM and was imported into the MDM Portal [ 25 ]. This process included semantic annotation with Unified Medical Language System (UMLS) codes [ 26 ] by medical experts. Since this is a laborious process, we wanted to integrate this valuable information into the SHIP database.…”

PhenCards: a data resource linking human phenotype information to biomedical knowledge

“…Some resources require regular updates and downloads to stay abreast of changes, but the majority of resources are API or web-based and require no changes to stay up-to-date Resource(s) Method of access Update needed? Content HPO [ 4 , 13 ] (includes OMIM [ 3 , 21 ] and Orphanet [ 2 ]), Disease Ontology [ 19 , 20 ] Elasticsearch [ 25 ] on indexed database Yes, monthly Standardized phenotype and disease terms ICD-10 [16], UMLS [ 17 , 18 ], OHDSI ATHENA [ 15 ], MeSH [ 14 ] Elasticsearch on indexed database Yes, yearly Standardized phenotype and disease terms Pharos (disease) [ 30 ] API No Disease aliases, expression, drug, pathway, Gene Ontology data IRS (Internal Revenue Service), Open990 Elasticsearch on indexed database Yes, yearly Nonprofit grants and foundations NIH (National Institute of Health) Federal Reporter, NIH FOAs (funding opportunity announcements) API No Federal grant and projects Direct2Experts [ 31 ] API No Collaborators, specialty physicians openFDA [ 32 ], Tocris, APExBio, Pharos (target) [ 30 ], DrugCentral [ 33 ] API No Federal and company drug, drug target and adverse effect data Pathway Commons [ 34 ] and KEGG [ 5 , 34 ] API No Pathways: diseases, biological functions ClinicalTrials.gov [ 35 ] API No …”

“…Phenotype vocabularies are superb tools for facilitating the investigation and classification of genetic diseases. Copious web servers, databases, and other resources for phenotypic terms and diseases exist: HPO (Human Phenotype Ontology) [ 4 , 13 ], MeSH (Medical Subject Headings) [ 14 ], OHDSI (Observational Health Data Sciences and Informatics) [ 15 ], ICD-10 (International Classification of Diseases, version 10) [ 16 ], UMLS (Unified Medical Language System) [ 17 , 18 ], Disease Ontology [ 19 , 20 ], OMIM (Online Mendelian Inheritance in Man) [ 3 , 21 ], DECIPHER (DatabasE of Chromosomal Imbalance and Phenotype in Humans using Ensembl Resource) [ 22 ], and Orphanet [ 2 ]. However, few of them link related biomedical information to disease and phenotypic terms, and there are numerous gaps in information.…”

Something New and Different: The Unified Medical Language System

“…Much has been written about the UMLS project and the heavily used resources resulting from it [e.g., [9][10][11][12][13]. This chapter focuses on how Lindberg's thinking, preferred ways of working, and decision-making guided UMLS goals and development and on what made the UMLS markedly "new and different" and ahead of its time.…”