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BMC Medical Informatics and Decision Making

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Open Access 01-12-2019 | Technical advance

Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics

Authors: Hye Hyeon Kim, Yu Rang Park, Kye Hwa Lee, Young Soo Song, Ju Han Kim

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

Abstract

Background

The increasing use of common data elements (CDEs) in numerous research projects and clinical applications has made it imperative to create an effective classification scheme for the efficient management of these data elements. We applied high-level integrative modeling of entire clinical documents from real-world practice to create the Clinical MetaData Ontology (CMDO) for the appropriate classification and integration of CDEs that are in practical use in current clinical documents.

Methods

CMDO was developed using the General Formal Ontology method with a manual iterative process comprising five steps: (1) defining the scope of CMDO by conceptualizing its first-level terms based on an analysis of clinical-practice procedures, (2) identifying CMDO concepts for representing clinical data of general CDEs by examining how and what clinical data are generated with flows of clinical care practices, (3) assigning hierarchical relationships for CMDO concepts, (4) developing CMDO properties (e.g., synonyms, preferred terms, and definitions) for each CMDO concept, and (5) evaluating the utility of CMDO.

Results

We created CMDO comprising 189 concepts under the 4 first-level classes of Description, Event, Finding, and Procedure. CMDO has 256 definitions that cover the 189 CMDO concepts, with 459 synonyms for 139 (74.0%) of the concepts. All of the CDEs extracted from 6 HL7 templates, 25 clinical documents of 5 teaching hospitals, and 1 personal health record specification were successfully annotated by 41 (21.9%), 89 (47.6%), and 13 (7.0%) of the CMDO concepts, respectively. We created a CMDO Browser to facilitate navigation of the CMDO concept hierarchy and a CMDO-enabled CDE Browser for displaying the relationships between CMDO concepts and the CDEs extracted from the clinical documents that are used in current practice.

Conclusions

CMDO is an ontology and classification scheme for CDEs used in clinical documents. Given the increasing use of CDEs in many studies and real-world clinical documentation, CMDO will be a useful tool for integrating numerous CDEs from different research projects and clinical documents. The CMDO Browser and CMDO-enabled CDE Browser make it easy to search, share, and reuse CDEs, and also effectively integrate and manage CDEs from different studies and clinical documents.

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Richesson RL, Krischer J. Data standards in clinical research: gaps, overlaps, challenges and future directions. J Am Med Inform Assoc. 2007. https://doi.org/10.1197/jamia.M2470.PubMedPubMedCentralCrossRef

Lim Choi Keung SN, Zhao L, Rossiter J, McGilchrist M, Culross F, Ethier JF, Burgun A, Verheij RA, Khan N, Taweel A, Curcin V, Delaney BC, Arvanitis TN. Detailed clinical modeling approach to data extraction from heterogeneous data sources for clinical research. AMIA Jt Summits Transl Sci Proc. 2014;2014:55–9.PubMedPubMedCentral

Sharma DK, Solbrig HR, Tao C, Weng C, Chute CG, Jiang G. Building a semantic web-based metadata repository for facilitating detailed clinical modeling in cancer genome studies. J Biomed Semantics. 2017. https://doi.org/10.1186/s13326-017-0130-4.

Tennison J, O'Hara K, Shadbolt N. APECKS: using and evaluating a tool for ontology construction with internal and external KA support. Int J Hum Comput Stud. 2002;56:375–422.CrossRef

Roberts K, Rodriguez L, Shooshan SE, Demner-Fushman D. Automatic extraction and post-coordination of spatial relations in consumer language. AMIA Annu Symp Proc. 2015;2015:1083–92.PubMedPubMedCentral

Daniel C, Ouagne D, Sadou E, Paris N, Hussain S, Jaulent MC, Kalra D. Cross border semantic interoperability for learning health systems: the EHR4CR semantic resources and services. Learn Health Sys. 2017. https://doi.org/10.1002/lrh2.10014.PubMedPubMedCentralCrossRef

Nadkarni PM, Brandt CA. The common data elements for cancer research: remarks on functions and structure. Methods Inf Med. 2006;45:594–601.PubMedPubMedCentralCrossRef

Ngouongo SM, Löbe M, Stausberg J. The ISO/IEC 11179 norm for metadata registries: does it cover healthcare standards in empirical research? J Biomed Inform. 2013. https://doi.org/10.1016/j.jbi.2012.11.008.PubMedCrossRef

Richesson RL, Nadkarni P. Data standards for clinical research data collection forms: current status and challenges. J Am Med Inform Assoc. 2011;18:341–6.PubMedPubMedCentralCrossRef

10.

Lin CH, Wu NY. Liou DM. A multi-technique approach to bridge electronic case report form design and data standard adoption. J Biomed Inform. 2015. https://doi.org/10.1016/j.jbi.2014.08.013. PubMedCrossRef

11.

The National Cancer Institute CDE Browser Tool. US National Institutes of Health (NIH). https://cdebrowser.nci.nih.gov/cdebrowserClient/cdeBrowser.html#/search. Accessed 6 Mar 2019.

12.

Jiang G, Solbrig HR, Chute CG. Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups. J Am Med Inform Assoc. 2012. https://doi.org/10.1136/amiajnl-2011-000739. PubMedPubMedCentralCrossRef

13.

Grinnon ST, Miller K, Marler JR, Lu Y, Stout A, Odenkirchen J, Kunitz S. National Institute of Neurological Disorders and Stroke common data element project - approach and methods. Clin Trials. 2012. https://doi.org/10.1177/1740774512438980.CrossRef

14.

Saver JL, Warach S, Janis S, et al. Standardizing the structure of stroke clinical and epidemiologic research data: the National Institute of Neurological Disorders and Stroke (NINDS) stroke common data element (CDE) project. Stroke. 2012. https://doi.org/10.1161/STROKEAHA.111.634352.PubMedPubMedCentralCrossRef

15.

Loring DW, Lowenstein DH, Barbaro NM, et al. Common data elements in epilepsy research: development and implementation of the NINDS epilepsy CDE project. Epilepsia. 2011. https://doi.org/10.1111/j.1528-1167.2011.03018.x.PubMedPubMedCentralCrossRef

16.

Min H, Ohira R, Collins MA, et al. Sharing behavioral data through a grid infrastructure using data standards. J Am Med Inform Assoc. 2014. https://doi.org/10.1136/amiajnl-2013-001763.PubMedCrossRef

17.

Barton C, Kallem C, Van Dyke P, Mon D, Richesson R. Demonstrating "collect once, use many"--assimilating public health secondary data use requirements into an existing domain analysis model. AMIA Annu Symp Proc. 2011;2011:98–107.PubMedPubMedCentral

18.

Park YR, Yoon YJ, Kim HH, Kim JH. Establishing semantic interoperability of biomedical metadata registries using extended semantic relationships. Stud Health Technol Inform. 2013;192:618–21.PubMed

19.

Park YR, Kim JH. Metadata registry and management system based on ISO 11179 for Cancer clinical trials information system. AMIA Annu Symp Proc. 2006:1056.

20.

Sinaci AA, Laleci Erturkmen GB. A federated semantic metadata registry framework for enabling interoperability across clinical research and care domains. J Biomed Inform. 2013. https://doi.org/10.1016/j.jbi.2013.05.009.PubMedCrossRef

21.

Jeong S, Kim HH, Park YR, Kim JH. Clinical data element ontology for unified indexing and retrieval of data elements across multiple metadata registries. Healthc Inform Res. 2014. https://doi.org/10.4258/hir.2014.20.4.295.PubMedPubMedCentralCrossRef

22.

Park YR, Yoon YJ, Jang TH, Seo HJ, Kim JH. CCR+: metadata based extended personal health record data model interoperable with the ASTM CCR standard. Healthc Inform Res. 2014. https://doi.org/10.4258/hir.2014.20.1.39.PubMedPubMedCentralCrossRef

23.

Ku HS, Kim S, Kim H, Chung HJ, Park YR, Kim JH. DialysisNet: application for integrating and management data sources of hemodialysis information by continuity of care record. Healthc Inform Res. 2014. https://doi.org/10.4258/hir.2014.20.2.145.PubMedPubMedCentralCrossRef

24.

Kim JH. Health avatar: An informatics platform for personal and private big data. Healthc Inform Res. 2014. https://doi.org/10.4258/hir.2014.20.1.1.PubMedPubMedCentralCrossRef

25.

Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shabo Shvo A. HL7 clinical document architecture, release 2. J Am Med Inform Assoc. 2006. https://doi.org/10.1197/jamia.M1888.PubMedPubMedCentralCrossRef

26.

Park YR, Kim H, An EY, et al. Establishing semantic interoperability in the course of clinical document exchange using international standard for metadata registry. J Korean Med Assoc. 2012. https://doi.org/10.5124/jkma.2012.55.8.729.CrossRef

27.

Obrst L. Ontological architectures. In: Poli R, Healy M, Kameas A, editors. Theory and applications of ontology: computer applications. Dordrecht: Springer; 2010. p. 27–66.CrossRef

28.

Xu L, Furlotte N, Lin Y, Heinrich K, Berry MW, George EO, Homayouni R. Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts. PLoS One. 2011. https://doi.org/10.1371/journal.pone.0018851.PubMedPubMedCentralCrossRef

29.

https://www.webmd.com/. Accessed 6 2019.

30.

Tudorache T, Nyulas C, Noy NF, Musen MA. WebProtégé: a collaborative ontology editor and knowledge acquisition tool for the web. Semant Web. 2013;4(1):89–99.PubMedPubMedCentral

31.

Whetzel PL, Noy NF, Shah NH, Alexander PR, Nyulas C, Tudorache T, Musen MA. BioPortal: enhanced functionality via new web services from the National Center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Res. 2011. https://doi.org/10.1093/nar/gkr469. PubMedPubMedCentralCrossRef

32.

http://bioportal.bioontology.org/ontologies/CMDO/. Accessed 6 Mar 2019.

33.

Chen ES, Melton GB, Engelstad ME, Sarkar IN. Standardizing clinical document names using the HL7/LOINC document ontology and LOINC codes. AMIA Annu Symp Proc. 2010;2010:101–5.PubMedPubMedCentral

34.

Hyun S, Shapiro JS, Melton G, Schlegel C, Stetson PD, Johnson SB, Bakken S. Iterative evaluation of the health level 7--logical observation identifiers names and codes clinical document ontology for representing clinical document names: a case report. J Am Med Inform Assoc. 2009. https://doi.org/10.1197/jamia.M2821.PubMedPubMedCentralCrossRef

Title: Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics
Authors: Hye Hyeon Kim
Yu Rang Park
Kye Hwa Lee
Young Soo Song
Ju Han Kim
Publication date: 01-12-2019
Publisher: BioMed Central
Published in: BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-019-0877-x

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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