• Logo
  • HamaraJournals

Developing Cardiac Electrophysiology Ontology: Moving Towards Data Harmonization and Integration

Hadi Kazemi-Arpanahi, Mostafa Shanbehzadeh, Saeed Jelvay, Hassan Bostan
87

Views


Abstract

Introduction: Cardiac electrophysiology (EP) studies the electrical heart conduction system which is used for diagnosis and treatment of cardiac arrhythmias. In this context, a huge amount of data is generated, requiring efficient and effective access, interpretation, and data analysis from multiple sources in a unified view. To resolve this challenge, this essay presents an ontology to reconcile data heterogeneity problems in this domain.

Material and Methods: The cardiac EP ontology was constructed according to the life cycle of ontology building. Structural, functional, and expert evaluation was performed to ensure its quality and usability.     

Results: Cardiac EP ontology was developed using protégé environment and implemented in OWL editing tool. It presented a detailed hierarchical structure of the cardiac EP domain with around 324 instances describing cardiac EP-related concepts.

Conclusion: Cardiac EP ontology provides an explicit formal description of the concepts, relationships, and properties associated with cardiac electrophysiology making seamless data integration between multiple heterogeneous databases. It also is a useful framework for knowledge representation in knowledge-based systems, as well as for explicit communication between experts in the EP domain.


References

Braithwaite J, Churruca K, Long JC, Ellis LA, Herkes J. When complexity science meets implementation science: A theoretical and empirical analysis of systems change. BMC Med. 2018; 16(1): 63. PMID: 29706132 DOI: 10.1186/s12916-018-1057-z

Misra G, Kumar V, Agarwal A, Agarwal K. Internet of things (IoT): A technological analysis and survey on vision, concepts, challenges, innovation directions, technologies, and applications (an upcoming or future generation computer communication system technology). American Journal of Electrical and Electronic Engineering. 2016; 4(1): 23-32.

Lee K, Rho S, Lee S-W. A method of extracting ontology module using concept relations for sharing knowledge in mobile cloud computing environment. Scientific World Journal. 2014; 2014: 382797. PMID: 25250374 DOI: 10.1155/2014/382797

Walinjkar A, Woods J. Personalized wearable systems for real-time ECG classification and healthcare interoperability: Real-time ECG classification and FHIR interoperability. Internet Technologies and Applications (ITA). IEEE; 2017.

Rittenhouse DR, Ramsay PP, Casalino LP, McClellan S, Kandel ZK, Shortell SM. Increased health information technology adoption and use among small primary care physician practices over time: A national cohort study. Ann Fam Med. 2017; 15(1): 56-62. PMID: 28376461 DOI: 10.1370/afm.1992

Yamada DB, Yoshiura VT, Miyoshi NSB, de Lima IB, Shinoda GYU, Rijo RPCL, et al. Proposal of an ontology for Mental Health Management in Brazil. Procedia computer science. 2018; 138: 137-42.

Kang Y, Fink JC, Doerfler R, Zhou L. Disease specific ontology of adverse events: Ontology extension and adaptation for chronic kidney disease. Comput Biol Med. 2018; 101: 210-7. PMID: 30195820 DOI: 10.1016/j.compbiomed.2018.08.024

Bona JP, Prior FW, Zozus MN, Brochhausen M. Enhancing clinical data and clinical research data with biomedical ontologies-insights from the knowledge representation perspective. Yearb Med Inform. 2019; 28(1): 140-51. PMID: 31419826 DOI: 10.1055/s-0039-1677912

Liyanage H, Krause P, de Lusignan S. Using ontologies to improve semantic interoperability in health data. J Innov Health Inform. 2015; 22(2): 309-15. PMID: 26245245 DOI: 10.14236/jhi.v22i2.159

Zhang H, Guo Y, Li Q, George TJ, Shenkman EA, Bian J, editors. Data integration through ontology-based data access to support integrative data analysis: A case study of cancer survival. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2017; 2017: 1300-3. PMID: 29707415 DOI: 10.1109/BIBM.2017.8217849

Gruber TR. Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies. 1995; 43(5-6): 907-28.

Hsieh N-C, Lee K-C, Chen W. The transformation of surgery patient care with a clinical research information system. Expert Systems with Applications. 2013; 40(1): 211-21.

Lapatas V, Stefanidakis M, Jimenez RC, Via A, Schneider MV. Data integration in biological research: An overview. J Biol Res (Thessalon). 2015; 22(1): 9. PMID: 26336651 DOI: 10.1186/s40709-015-0032-5

Sahoo SS, Zhang G-Q, Bamps Y, Fraser R, Stoll S, Lhatoo SD, et al. Managing information well: Toward an ontology-driven informatics platform for data sharing and secondary use in epilepsy self-management research centers. Health Informatics J. 2016; 22(3): 548-61. PMID: 25769938 DOI: 10.1177/1460458215572924

Waqialla M, Alshammari R, Razzak MI. An ontology for remote monitoring of cardiac implantable electronic devices. International Conference on Computer, Communications, and Control Technology. IEEE; 2015.

Azami-Aghdash S, Ghojazadeh M, Naghavi-Behzad M, Imani S, Aghaei MH. Perspectives of cardiac care unit nursing staff about developing hospice services in iran for terminally ill cardiovascular patients: A qualitative study. Indian J Palliat Care. 2015; 21(1): 56–60. PMID: 25709187 DOI: 10.4103/0973-1075.150185

Hassan A, Tan NY, Aung H, Connolly HM, Hodge DO, Vargas ER, et al. Outcomes of atrial arrhythmia radiofrequency catheter ablation in patients with Ebstein’s anomaly. Europace. 2018; 20(3): 535-40. PMID: 28340054 DOI: 10.1093/europace/euw396

Thomas D, Christ T, Fabritz L, Goette A, Hammwöhner M, Heijman J, et al. German cardiac society working group on cellular electrophysiology state-of-the-art paper: Impact of molecular mechanisms on clinical arrhythmia management. Clin Res Cardiol. 2019; 108(6): 577-99. PMID: 30306295 DOI: 10.1007/s00392-018-1377-1

Rosier A, Mabo P, Temal L, Van Hille P, Dameron O, Deleger L, et al. Remote monitoring of cardiac implantable devices: Ontology driven classification of the alerts. In: Hofdijk J, Lovis C, Ehrler F, Sieverink F, Ugon A, Hercigonja-Szekeres M (Eds.). Transforming healthcare with the Internet of things. IOS Press; 2016.

Slotwiner DJ. Electronic health records and cardiac implantable electronic devices: New paradigms and efficiencies. J Interv Card Electrophysiol. 2016; 47(1): 29-35. PMID: 27585791 DOI: 10.1007/s10840-016-0170-1

Slotwiner DJ, Abraham RL, Al-Khatib SM, Anderson HV, Bunch TJ, Ferrara MG, et al. HRS white paper on interoperability of data from cardiac implantable electronic devices (CIEDs). Heart Rhythm. 2019; 16(9): e107-27. PMID: 31077801 DOI: 10.1016/j.hrthm.2019.05.002

Quinn TA, Granite S, Allessie MA, Antzelevitch C, Bollensdorff C, Bub G, et al. Minimum information about a cardiac electrophysiology experiment (MICEE): Standardised reporting for model reproducibility, interoperability, and data sharing. Prog Biophys Mol Biol. 2011; 107(1): 4-10. PMID: 21745496 DOI: 10.1016/j.pbiomolbio.2011.07.001

Van der Velde E, Foeken H, Witteman T, van Erven L, Schalij M. Integration of data from remote monitoring systems and programmers into the hospital electronic health record system based on international standards. Neth Heart J. 2012; 20(2): 66-70. PMID: 22231151 DOI: 10.1007/s12471-011-0234-x

Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, et al. The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol. 2007; 25(11): 1251-5. PMID: 17989687 DOI: 10.1038/nbt1346

Fernández-López M, Gómez-Pérez A, Juristo N. Methontology: From ontological art towards ontological engineering. Ontological Engineering Symposium. Stanford University, EEUU; 1997.

López MF, Gómez-Pérez A, Sierra JP, Sierra AP. Building a chemical ontology using methontology and the ontology design environment. IEEE Intelligent Systems and their applications. 1999; 14(1): 37-46.

Younesi E, Malhotra A, Gündel M, Scordis P, Page M, Müller B, et al. PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain. Theor Biol Med Model. 2015; 12: 20. PMID: 26395080 DOI: 10.1186/s12976-015-0017-y

Malhotra A, Younesi E, Gündel M, Müller B, Heneka MT, Hofmann-Apitius M. ADO: A disease ontology representing the domain knowledge specific to Alzheimer's disease. Alzheimers Dement. 2014; 10(2): 238-46. PMID: 23830913 DOI: 10.1016/j.jalz.2013.02.009

Musen MA. The protégé project: A look back and a look forward. AI Matters. 2015; 1(4): 4–12. PMID: 27239556 DOI: 10.1145/2757001.2757003

Amith M, He Z, Bian J, Lossio-Ventura JA, Tao C. Assessing the practice of biomedical ontology evaluation: Gaps and opportunities. J Biomed Inform. 2018; 80: 1-13. PMID: 29462669 DOI: 10.1016/j.jbi.2018.02.010

Mathur I, Darbari H, Joshi N, editors. Domain ontology development for communicable diseases. International Conference on Artificial Intelligence, Soft Computing. Citeseer; 2013.

Alizadeh M, Shahrezaei MH, Tahernezhad-Javazm F. Ontology based information integration: A survey. arXiv. 2019; 190913762.

Ekaputra F, Sabou M, Serral Asensio E, Kiesling E, Biffl S. Ontology-based data integration in multi-disciplinary engineering environments: A review. Open Journal of Information Systems. 2017; 4(1): 1-26.

Zhang H, Guo Y, Li Q, George TJ, Shenkman E, Modave F, et al. An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival. BMC Med Inform Decis Mak. 2018; 18(Suppl 2): 41. PMID: 30066664 DOI: 10.1186/s12911-018-0636-4

Louie B, Mork P, Martin-Sanchez F, Halevy A, Tarczy-Hornoch P. Data integration and genomic medicine. J Biomed Inform. 2007; 40(1): 5-16. PMID: 16574494 DOI: 10.1016/j.jbi.2006.02.007

Buccella A, Cechich A, Rodríguez Brisaboa N. An ontology approach to data integration. Journal of Computer Science & Technology. 2003; 3(2): 62-8.

Min H, Manion FJ, Goralczyk E, Wong Y-N, Ross E, Beck JR. Integration of prostate cancer clinical data using an ontology. J Biomed Inform. 2009; 42(6): 1035-45. PMID: 19497389 DOI: 10.1016/j.jbi.2009.05.007

Hotchkiss J, Manyisa N, Adadey SM, Oluwole OG, Wonkam E, Mnika K, et al. The hearing impairment ontology: A tool for unifying hearing impairment knowledge to enhance collaborative research. Genes (Basel). 2019; 10(12): 960. PMID: 31766582 DOI: 10.3390/genes10120960

Mate S, Köpcke F, Toddenroth D, Martin M, Prokosch H-U, Bürkle T, et al. Ontology-based data integration between clinical and research systems. PLoS One. 2015; 10(1): e0116656. PMID: 25588043 DOI: 10.1371/journal.pone.0116656

Serra LM, Duncan WD, Diehl AD. An ontology for representing hematologic malignancies: the cancer cell ontology. BMC Bioinformatics. 2019; 20(Suppl 5): 181. PMID: 31272372 DOI: 10.1186/s12859-019-2722-8

Rosier A, Mabo P, Chauvin M, Burgun A. An ontology-based annotation of cardiac implantable electronic devices to detect therapy changes in a national registry. IEEE Journal of Biomedical and Health Informatics. 2014; 19(3): 971-8.

Doing-Harris K, Bray BE, Thackeray A, Shah RU, Shao Y, Cheng Y, et al. Development of a cardiac-centered frailty ontology. J Biomed Semantics. 2019; 10(1): 3. PMID: 30658684 DOI: 10.1186/s13326-019-0195-3

Soguero-Ruiz C, Lechuga-Suárez L, Mora-Jiménez I, Ramos-López J, Barquero-Perez O, Garcia-Alberola A, et al. Ontology for heart rate turbulence domain from the conceptual model of SNOMED-CT. IEEE Trans Biomed Eng. 2013; 60(7): 1825-33. PMID: 23372067 DOI: 10.1109/TBME.2013.2243147

Martínez-Romero M, Vázquez-Naya JM, Pereira J, Pereira M, Pazos A, Baños G. The iOSC3 system: Using ontologies and SWRL rules for intelligent supervision and care of patients with acute cardiac disorders. Comput Math Methods Med. 2013; 2013: 650671. PMID: 23476717 DOI: 10.1155/2013/650671

Noy NF, McGuinness DL. Ontology development 101: A guide to creating your first ontology. Technical Report KSL-01-05 and SMI-2001-0880, Stanford Knowledge Systems Laboratory and Stanford Medical Informatics, 2001.




DOI: http://dx.doi.org/10.30699/fhi.v9i1.231

Refbacks

  • There are currently no refbacks.