Skip to main content
Top
Published in: BMC Medical Informatics and Decision Making 1/2018

Open Access 01-12-2018 | Research article

SNOMED CT standard ontology based on the ontology for general medical science

Authors: Shaker El-Sappagh, Francesco Franda, Farman Ali, Kyung-Sup Kwak

Published in: BMC Medical Informatics and Decision Making | Issue 1/2018

Login to get access

Abstract

Background

Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is a comprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic health data. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but these efforts have been hampered by the size and complexity of SCT.

Method

Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the terms in SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks of definitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-level SCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS).

Results

The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. The approach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundry ontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-level ontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555 annotations. It is publicly available through the bioportal at http://bioportal.bioontology.org/ontologies/SCTO/ .

Conclusion

The resulting ontology can enhance the semantics of clinical decision support systems and semantic interoperability among distributed electronic health records. In addition, the populated ontology can be used for the automation of mobile health applications.
Literature
1.
go back to reference Arp A, Smith B, Spear A, Building ontologies with basic formal ontology, the MIT press, 2015. Arp A, Smith B, Spear A, Building ontologies with basic formal ontology, the MIT press, 2015.
2.
go back to reference Lee D, Cornet R, Lau F, De Keizer N. A survey of SNOMED CT implementations. J Biomed Inform. 2013;46(1):87–96.CrossRefPubMed Lee D, Cornet R, Lau F, De Keizer N. A survey of SNOMED CT implementations. J Biomed Inform. 2013;46(1):87–96.CrossRefPubMed
4.
go back to reference Bhattacharyya S. Introduction to SNOMED CT. Singapore: Springer Science. 2016.CrossRef Bhattacharyya S. Introduction to SNOMED CT. Singapore: Springer Science. 2016.CrossRef
5.
go back to reference Saitwal H, Qing D, Jones S, Bernstam E, Chute C, Johnson T. Cross-terminology mapping challenges: a demonstration using medication terminological systems. J Biomed Inform. 2012;45:613–25.CrossRefPubMedPubMedCentral Saitwal H, Qing D, Jones S, Bernstam E, Chute C, Johnson T. Cross-terminology mapping challenges: a demonstration using medication terminological systems. J Biomed Inform. 2012;45:613–25.CrossRefPubMedPubMedCentral
7.
go back to reference Ochs C, Case J, Perl Y. Analyzing structural changes in SNOMED CT’s bacterial infectious diseases using a visual semantic delta. J Biomed Inform. 2017;67:101–16.CrossRefPubMedPubMedCentral Ochs C, Case J, Perl Y. Analyzing structural changes in SNOMED CT’s bacterial infectious diseases using a visual semantic delta. J Biomed Inform. 2017;67:101–16.CrossRefPubMedPubMedCentral
8.
go back to reference Perez-Rey D, Alonso-Calvo R, Paraiso-Medina S, Munteanu C, Garcia-Remesal M. SNOMED2HL7: a tool to normalize and bind SNOMED CT concepts to the HL7 reference information model. Comput Methods Prog Biomed. 2017;149:1–9.CrossRef Perez-Rey D, Alonso-Calvo R, Paraiso-Medina S, Munteanu C, Garcia-Remesal M. SNOMED2HL7: a tool to normalize and bind SNOMED CT concepts to the HL7 reference information model. Comput Methods Prog Biomed. 2017;149:1–9.CrossRef
10.
go back to reference Ivanovic M, Budimac Z. An overview of ontologies and data resources in medical domains. Expert Syst Appl. 2014;41:5158–66.CrossRef Ivanovic M, Budimac Z. An overview of ontologies and data resources in medical domains. Expert Syst Appl. 2014;41:5158–66.CrossRef
11.
go back to reference Yamagata Y, Kozaki K, Imai T, Ohe K, Mizoguchi R. An ontological modeling approach for abnormal states and its application in the medical domain. Journal of Biomedical Semantics. 2014;5:23.CrossRefPubMedPubMedCentral Yamagata Y, Kozaki K, Imai T, Ohe K, Mizoguchi R. An ontological modeling approach for abnormal states and its application in the medical domain. Journal of Biomedical Semantics. 2014;5:23.CrossRefPubMedPubMedCentral
12.
go back to reference Isern D, Sanchez D, Antonio MA. Ontology-driven execution of clinical guidelines. Comput Methods Prog Biomed. 2012;107:122–39.CrossRef Isern D, Sanchez D, Antonio MA. Ontology-driven execution of clinical guidelines. Comput Methods Prog Biomed. 2012;107:122–39.CrossRef
13.
go back to reference Gruber T. A translation approach to portable ontologies. Knowl Acquis. 1995;5(2):199–220.CrossRef Gruber T. A translation approach to portable ontologies. Knowl Acquis. 1995;5(2):199–220.CrossRef
14.
go back to reference Dentler K, Cornet R. Redundant elements in SNOMED CT concept definitions. AIME, Springer-Verlag Berlin Heidelberg. 2013:186–95. Dentler K, Cornet R. Redundant elements in SNOMED CT concept definitions. AIME, Springer-Verlag Berlin Heidelberg. 2013:186–95.
15.
go back to reference Zhang M, Patrick J, Truran D, Innes K. Deriving a SNOMED CT data model. In Proceedings of the First Semantic Mining Conference on SNOMED CT. 2006:59–63. Zhang M, Patrick J, Truran D, Innes K. Deriving a SNOMED CT data model. In Proceedings of the First Semantic Mining Conference on SNOMED CT. 2006:59–63.
16.
go back to reference Bodenreider O, Smith B, Kumar A, Burgun A. Investigating subsumption in SNOMED CT: an exploration into large description logic-based biomedical terminologies. Artif. Intell. Med. 2007;39(3):183–195. Bodenreider O, Smith B, Kumar A, Burgun A. Investigating subsumption in SNOMED CT: an exploration into large description logic-based biomedical terminologies. Artif. Intell. Med. 2007;39(3):183–195.
17.
19.
go back to reference Oluoch T, de Keizer N, Langat P, Alaska I, Ochieng K, Okeyo N, Kwaro D, Cornet R. A structured approach to recording AIDS-defining illnesses in Kenya: a SNOMED CT based solution. J Biomed Inform. 2015;56:387–94.CrossRefPubMedPubMedCentral Oluoch T, de Keizer N, Langat P, Alaska I, Ochieng K, Okeyo N, Kwaro D, Cornet R. A structured approach to recording AIDS-defining illnesses in Kenya: a SNOMED CT based solution. J Biomed Inform. 2015;56:387–94.CrossRefPubMedPubMedCentral
20.
go back to reference Campbell W, Pedersen J, McClay J, Rao P, Bastola D, Campbell J. An alternative database approach for management of SNOMED CT and improved patient data queries. J Biomed Inform. 2015;57:350–7.CrossRefPubMed Campbell W, Pedersen J, McClay J, Rao P, Bastola D, Campbell J. An alternative database approach for management of SNOMED CT and improved patient data queries. J Biomed Inform. 2015;57:350–7.CrossRefPubMed
21.
go back to reference Bakhshi-Raiez F, de Keizer N, Cornet R, Dorrepaal M, Dongelmans D, Jaspers M. A usability evaluation of a SNOMED CT based compositional interface terminology for intensive care. Int J Med Inform. 2012;81:351–62.CrossRefPubMed Bakhshi-Raiez F, de Keizer N, Cornet R, Dorrepaal M, Dongelmans D, Jaspers M. A usability evaluation of a SNOMED CT based compositional interface terminology for intensive care. Int J Med Inform. 2012;81:351–62.CrossRefPubMed
22.
go back to reference Sir M, Bradac Z, Zdenek P. Ontology versus Database. IFAC. 2015;48(4):220–5. Sir M, Bradac Z, Zdenek P. Ontology versus Database. IFAC. 2015;48(4):220–5.
23.
go back to reference Biskup J, Bring M, Bulinski M. Inference control of open relational queries under closed-world semantics based on theorem proving. Inf Syst. 2017;70:32–47.CrossRef Biskup J, Bring M, Bulinski M. Inference control of open relational queries under closed-world semantics based on theorem proving. Inf Syst. 2017;70:32–47.CrossRef
24.
go back to reference Schadow G, Barnes M, McDonald C. Representing and querying conceptual graphs with relational database management systems is possible. in: Proc AMIA Symp. 2001:598–602. Schadow G, Barnes M, McDonald C. Representing and querying conceptual graphs with relational database management systems is possible. in: Proc AMIA Symp. 2001:598–602.
26.
go back to reference Dentler K, Cornet R, ten Teije A, de Keizer N. Comparison of Reasoners for large ontologies in the OWL 2 EL profile. Semantic Web Journal. 2011;2(2):71–87. Dentler K, Cornet R, ten Teije A, de Keizer N. Comparison of Reasoners for large ontologies in the OWL 2 EL profile. Semantic Web Journal. 2011;2(2):71–87.
27.
go back to reference Souvignet J, Declerck G, Asfari H, Jaulent M, Bousquet C. OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval. J Biomed Inform. 2016;63:100–7.CrossRefPubMed Souvignet J, Declerck G, Asfari H, Jaulent M, Bousquet C. OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval. J Biomed Inform. 2016;63:100–7.CrossRefPubMed
28.
go back to reference Schulz S, Cornet R, Spackman K. Consolidating SNOMED CT’s ontological commitment. Appl Ontol. 2011;6(1):1–11. Schulz S, Cornet R, Spackman K. Consolidating SNOMED CT’s ontological commitment. Appl Ontol. 2011;6(1):1–11.
29.
go back to reference Penaloza R, Sertkaya B. Understanding the complexity of axiom pinpointing in lightweight description logics. Artif Intell. 2017;250:80–104.CrossRef Penaloza R, Sertkaya B. Understanding the complexity of axiom pinpointing in lightweight description logics. Artif Intell. 2017;250:80–104.CrossRef
30.
go back to reference Gao Y, Khazai R. SNOMED CT Concept Model: IHTSDO – International Health Terminology Standards Development Organization; 2015. Gao Y, Khazai R. SNOMED CT Concept Model: IHTSDO – International Health Terminology Standards Development Organization; 2015.
31.
go back to reference Schulz S, Martínez-Costa C. Harmonizing SNOMED CT with BioTopLite: An Exercise in Principled Ontology Alignment. MEDINFO 2015: eHealth-enabled Health, IMIA and IOS Press. 2015;216:–832. Schulz S, Martínez-Costa C. Harmonizing SNOMED CT with BioTopLite: An Exercise in Principled Ontology Alignment. MEDINFO 2015: eHealth-enabled Health, IMIA and IOS Press. 2015;216:–832.
34.
go back to reference Schulz S, Boeker M. BioTopLite: an upper level ontology for the life sciences evolution. Design and Application In GI-Jahrestagung. 2013:1889–99. Schulz S, Boeker M. BioTopLite: an upper level ontology for the life sciences evolution. Design and Application In GI-Jahrestagung. 2013:1889–99.
35.
go back to reference Rodrigues J, et al. ICD-11 and SNOMED CT common ontology: circulatory system. MIE. 2014:1043–7. Rodrigues J, et al. ICD-11 and SNOMED CT common ontology: circulatory system. MIE. 2014:1043–7.
36.
go back to reference Chen C, Chang C, Peng Y, Poon S, Huang S, Li Y. Effect of implementation of a coded problem list entry subsystem. Comput Methods Programs Biomed. 2016;134:1–9.CrossRefPubMed Chen C, Chang C, Peng Y, Poon S, Huang S, Li Y. Effect of implementation of a coded problem list entry subsystem. Comput Methods Programs Biomed. 2016;134:1–9.CrossRefPubMed
37.
38.
go back to reference Cimino J. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med. 1998;37(4–5):394.PubMedPubMedCentral Cimino J. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med. 1998;37(4–5):394.PubMedPubMedCentral
39.
go back to reference Rector A. Clinical terminology: why is it so hard? Methods Inf Med. 1999;38(4/5):239–52.PubMed Rector A. Clinical terminology: why is it so hard? Methods Inf Med. 1999;38(4/5):239–52.PubMed
42.
go back to reference Ceusters W, Smith B. Biomarkers in the Ontology for General Medical Science. European Federation for Medical Informatics (EFMI). 2015;210:155–9. Ceusters W, Smith B. Biomarkers in the Ontology for General Medical Science. European Federation for Medical Informatics (EFMI). 2015;210:155–9.
44.
go back to reference Scheuermann R, Ceusters W, Smith B. Toward an ontological treatment of disease and diagnosis. Summit Transl Bioinform. 2009;2009:116–20.PubMedPubMedCentral Scheuermann R, Ceusters W, Smith B. Toward an ontological treatment of disease and diagnosis. Summit Transl Bioinform. 2009;2009:116–20.PubMedPubMedCentral
45.
go back to reference Dentler K, Cornet R, Teije A, de Keizer N. Comparison of Reasoners for large ontologies in the OWL 2 EL profile. IOS Press Semantic Web. 2011;1:1–5. Dentler K, Cornet R, Teije A, de Keizer N. Comparison of Reasoners for large ontologies in the OWL 2 EL profile. IOS Press Semantic Web. 2011;1:1–5.
46.
go back to reference El-Sappagh S, El-Masri S, Elmogy M, Riad A. A diabetes diagnostic domain ontology for CBR system from the conceptual model of SNOMED CT. IEEE International Conference on Engineering and Technology (ICET). 2014:1–7. El-Sappagh S, El-Masri S, Elmogy M, Riad A. A diabetes diagnostic domain ontology for CBR system from the conceptual model of SNOMED CT. IEEE International Conference on Engineering and Technology (ICET). 2014:1–7.
47.
go back to reference Hussain M, et al. Cloud-based smart CDSS for chronic diseases. Health Technol. 2013;3:153–75.CrossRef Hussain M, et al. Cloud-based smart CDSS for chronic diseases. Health Technol. 2013;3:153–75.CrossRef
48.
go back to reference Peleg M, et al. MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains. User Model User-Adap Inter. 2017;27:159–213.CrossRef Peleg M, et al. MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains. User Model User-Adap Inter. 2017;27:159–213.CrossRef
49.
go back to reference Rodrigues J, Schulz S, Rector A, Spackman K, Üstün T, Chute C, Mea V, Millar J, Persson K. Sharing ontology between ICD 11 and SNOMED CT will enable seamless re-use and semantic interoperability. Stud Health Technol Inform. 2013;192:343–6.PubMed Rodrigues J, Schulz S, Rector A, Spackman K, Üstün T, Chute C, Mea V, Millar J, Persson K. Sharing ontology between ICD 11 and SNOMED CT will enable seamless re-use and semantic interoperability. Stud Health Technol Inform. 2013;192:343–6.PubMed
50.
go back to reference Cardillo E. Mapping between international medical terminologies, Annex 4 to SHN D3.3, 2015. Cardillo E. Mapping between international medical terminologies, Annex 4 to SHN D3.3, 2015.
53.
go back to reference He Z, Geller J, Chen Y. A comparative analysis of the density of the SNOMED CT conceptual content for semantic harmonization. Artif Intell Med. 2015;64:29–40.CrossRefPubMedPubMedCentral He Z, Geller J, Chen Y. A comparative analysis of the density of the SNOMED CT conceptual content for semantic harmonization. Artif Intell Med. 2015;64:29–40.CrossRefPubMedPubMedCentral
54.
go back to reference Spackman K, Dionne R, Mays E, Weis J. Role grouping as an extension to the description logic of Ontylog motivated by concept modeling in SNOMED. In Proceedings of the AMIA Symposium. Am Med Inform Assoc. 2002:712–6. Spackman K, Dionne R, Mays E, Weis J. Role grouping as an extension to the description logic of Ontylog motivated by concept modeling in SNOMED. In Proceedings of the AMIA Symposium. Am Med Inform Assoc. 2002:712–6.
55.
go back to reference Mary M, Soualmia L, Gansel X. Usability and Improvement of Existing Alignments: The LOINC-SNOMED CT Case Study: LNAI 10180, Springer International Publishing. 2017:145–8. Mary M, Soualmia L, Gansel X. Usability and Improvement of Existing Alignments: The LOINC-SNOMED CT Case Study: LNAI 10180, Springer International Publishing. 2017:145–8.
56.
go back to reference El-Sappagh S, Ali F. DDO: a diabetes mellitus diagnosis ontology. Applied Informatics. 2016;3(1):5.CrossRef El-Sappagh S, Ali F. DDO: a diabetes mellitus diagnosis ontology. Applied Informatics. 2016;3(1):5.CrossRef
57.
go back to reference El-Sappagh S, Kwak D, Ali F, Kwak KS. DMTO: a realistic ontology for standard diabetes mellitus treatment. Journal of biomedical semantics. 2018;9(1):8.CrossRefPubMedPubMedCentral El-Sappagh S, Kwak D, Ali F, Kwak KS. DMTO: a realistic ontology for standard diabetes mellitus treatment. Journal of biomedical semantics. 2018;9(1):8.CrossRefPubMedPubMedCentral
58.
go back to reference Souvignet J, Rodrigues J. Toward a Patient Safety Upper Level Ontology, 2015. Souvignet J, Rodrigues J. Toward a Patient Safety Upper Level Ontology, 2015.
59.
go back to reference Martínez-Costa C, Schulz S. Ontology-based reinterpretation of the SNOMED CT context model, In ICBO; 2013:90–5. Martínez-Costa C, Schulz S. Ontology-based reinterpretation of the SNOMED CT context model, In ICBO; 2013:90–5.
60.
go back to reference Cheetham E, Gao Y, Goldberg B, Hausam R, Schulz S. Formal representation of disorder associations in SNOMED CT. Proceedings of the 2015 International Conference on Biomedical Ontology (ICBO2015). 2015:27–31. Cheetham E, Gao Y, Goldberg B, Hausam R, Schulz S. Formal representation of disorder associations in SNOMED CT. Proceedings of the 2015 International Conference on Biomedical Ontology (ICBO2015). 2015:27–31.
61.
go back to reference Bodenreider O. Identifying missing hierarchical relations in SNOMED CT from logical definitions based on the lexical features of concept names. In ICBO/BioCreative, 2016. Bodenreider O. Identifying missing hierarchical relations in SNOMED CT from logical definitions based on the lexical features of concept names. In ICBO/BioCreative, 2016.
62.
go back to reference Hogan W. Aligning the Top Level of SNOMED-CT with Basic Formal Ontology. KR-MED 2008. 2008;7:113. Hogan W. Aligning the Top Level of SNOMED-CT with Basic Formal Ontology. KR-MED 2008. 2008;7:113.
63.
go back to reference Ochs C, Geller J, Perl Y, Chen Y, Agrawal A, Case J, Hripcsak G. A tribal abstraction network for SNOMED CT target hierarchies without attribute relationships. J Am Med Inform Assoc. 2015;22:628–39.CrossRefPubMed Ochs C, Geller J, Perl Y, Chen Y, Agrawal A, Case J, Hripcsak G. A tribal abstraction network for SNOMED CT target hierarchies without attribute relationships. J Am Med Inform Assoc. 2015;22:628–39.CrossRefPubMed
Metadata
Title
SNOMED CT standard ontology based on the ontology for general medical science
Authors
Shaker El-Sappagh
Francesco Franda
Farman Ali
Kyung-Sup Kwak
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2018
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-018-0651-5

Other articles of this Issue 1/2018

BMC Medical Informatics and Decision Making 1/2018 Go to the issue