=Paper=
{{Paper
|id=Vol-3805/ICBO-2022_paper_9978
|storemode=property
|title=Translating Medical Vocabularies via the Unified Medical Language Systems
|pdfUrl=https://ceur-ws.org/Vol-3805/ICBO-2022_paper_9978.pdf
|volume=Vol-3805
|authors=William T. Roddy,Daniel Olson,Diane Corey,Ian Braun,Terrence R. McHugh,Emily Hartley,Smith Heavner,Ramona L. Walls
|dblpUrl=https://dblp.org/rec/conf/icbo/RoddyOCBMHHW22
}}
==Translating Medical Vocabularies via the Unified Medical Language Systems==
https://ceur-ws.org/Vol-3805/ICBO-2022_paper_9978.pdf
Translating medical vocabularies via
the Unified Medical Language System
William T. Roddy1, Daniel Olson,1, Diane Corey1, Ian Braun1, Terrence R. McHugh1, Emily Hartley1,
Smith Heavner1, Ramona L. Walls1
1
Critical Path Institute, Tucson, AZ
Keywords 1
data management, OMOP, SDTM, CDISC, UMLS, terminology, vocabulary
1. Background Outcomes Partnership (OMOP) model prior to
data integration. The relevant concepts within the
integrated data will be further mapped to OBO
The Rare Disease Cures Accelerator – Data
ontologies (https://obofoundry.org/) prior to
and Analytics Platform (RDCA-DAP) of the
knowledge graph ingestion (which is outside the
Critical Path Institute (C-Path) is an FDA-funded
scope of this submission). The OMOP
effort to facilitate drug development for rare
standardized vocabulary includes many
diseases (https://portal.rdca.c-path.org/). RDCA-
biomedical terminologies that enable
DAP helps researchers leverage existing
standardization of source data; however, these
knowledge and analyze data to inform and
terminologies do not currently include the Study
optimize clinical trial design with new sources of
Data Tabulation Model (SDTM) controlled
evidence. The platform supports the use of data to
terminology which is frequently used for
improve the quantitative characterization of rare
submissions to regulatory authorities. Generating
disease progression, define novel biomarkers and
mappings between the SDTM terminology and
endpoints, and provides analytical tools to inform
the OMOP standardized vocabulary will further
the design of innovative trial protocols.
expand the capabilities of data sharing and reuse
One of the key deliverables is the creation of a
between real-world data sources and clinical trial
knowledge graph from the natural history, registry
data sources. We demonstrate an implementation
and clinical trial data received. The data, however,
of translating the terminology used in these two
must be cleaned and standardized prior to
Common Data Models.
knowledge graph ingestion which has presented
us with opportunities to implement novel (to our
organization) automation procedures of certain
data management activities (e.g. vocabulary 1.1. Methods
mappings).
Recent advances in clinical research data We have developed mappings between
standards have resulted in the development of SDTM terminology and the OMOP standardized
several Common Data Models (CDMs) which are vocabulary by using the Unified Medical
leveraged to support the sharing and reuse of Language System (UMLS). The SDTM
data1. Critical Path Institute has chosen to map terminology is published by the National Cancer
legacy data to the Observational Medical Institute Enterprise Vocabulary Services (NCI
ICBO 2022, September 25-28, 2022, Ann Arbor, MI, USA
EMAIL: wroddy@c-path.org (A. 1); dolson@c-path.org (A. 2);
dcorey@c-path.org (A. 3) ibraun@c-path.org (A. 4)
terrence.r.mchugh@gmail.com (A.5) ehartley@c-path.org (A.6)
sheavner@c-path.org (A. 7) rwalls@c-path.org (A. 8)
ORCID: 0000-0002-8453-520X (A. 1); 0000-0002-8134-1207 (A.
2); 0000-0003-3840-2315 (A. 3) 0000-0002-2389-9288 (A. 4)
0000-0002-3805-0359 (A.5) X (A.6) 0000-0003-0912-0407 (A. 7)
0000-0001-8815-0078 (A. 8)
©️ 2022 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
EVS), where concepts are identified by concept mappings require further refinement based on
codes (C-Codes) which are included within the subject-matter expert review and additional
UMLS. We identified all UMLS Concept Unique transformation logic, to ensure that context
Identifiers (CUIs) by searching for atoms (the appropriateness of mappings. For example, we
smallest unit of naming in a source) with a are exploring further refinement by including the
source abbreviation of NCI and a source code source SDTM domain in the mapping logic.
containing the C-Code in the SDTM Additionally, it may be possible to bolster the
terminology. The UMLS CUIs associated with mappings with additional resources such as
the SDTM terminology were used to retrieve CDISC’s LOINC to LB Mapping Files.
source codes originating from terminologies that
are included within the OMOP vocabulary. We
primarily focused on the SNOMED-CT, LOINC,
RxNorm, and UCUM vocabularies because they
are standard within the OMOP vocabulary, but
we also expanded the search to MedDRA and
MeSH due to their appreciable representation in
SDTM and OMOP. The source codes from these
vocabularies were used to identify the equivalent
OMOP vocabulary standard concept.
Figure 1: data flow with the number of unique C-
1.1.1. Status of Mapping Results Codes present in each
The 2021-12-17 release of the SDTM
terminology included 22,132 unique C-Codes. 2. Conclusion
Of these, 84.2% were available within the UMLS
2021AB release and 57.4% were only indexed in We show that it is feasible to aid the
an NCI EVS terminology or the Metathesaurus transformation process between CDMs by
vocabulary. Within the UMLS searched utilizing the UMLS to generate mappings
vocabularies 25.5% of the C-Codes were present between the SDTM terminology and the OMOP
and 1.3% were present in other vocabularies. We vocabularies. Future work will expand and
used all possible vocabulary codes to query the ensure accuracy of the mappings, outline
OMOP vocabularies (release v5.0 28-JAN-22) improvements of data standards for
and found that 19.2% of the C-Codes mapped to interoperability, and publish source code.
a standard OMOP concept. There were no
corresponding OMOP concepts for 4.6% of C- 3. References
Codes; however, nearly 90% of these are UCUM
concepts and this is expected based on the
OMOP documentation2. [1] Garza M, Del Fiol G, Tenenbaum J, Walden
To evaluate the applicability of this A, Zozus MN. Evaluating common data
approach, we used SDTM data from the C-Path models for use with a longitudinal
Online Data Repository3 to identify submission community registry. J Biomed Inform. 2016
values from controlled terminology codelists. We Dec;64:333-341.
found that a majority of observations mapped to [2] Available from:
at least one standard concept. The https://www.ohdsi.org/web/wiki/doku.php?i
appropriateness of initial mappings was assessed d=documentation%3Avocabulary%3Aucum
by comparing the SDTM codelist domain to the [3] Critical Path Institute Online Data
target concept domain. In many cases the source- Repository (CODR). Available from:
to-target domain were appropriate; for example, https://codr.c-path.org/
codes in the laboratory data (LB) domain had
target concepts in the Measurement domain.
This preliminary mapping shows potential in the
ability to extract translations between SDTM and
OMOP vocabularies through the UMLS. These