Abstract
Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data’ diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards’ requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism.
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References
Wearable Medical Device Market, http://www.marketsandmarkets.com/PressReleases/wearable-medical-device.asp. Accessed 20 November 2018.
Kwon, J., Kim, D., Park, W., Kim, L., A wearable device for emotional recognition using facial expression and physiological response. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. IEEE 5765–5768, 2016. https://doi.org/10.1109/EMBC.2016.7592037
Dionisi, A., Marioli, D., Sardini, E., and Serpelloni, M., Autonomous wearable system for vital signs measurement with energy-harvesting module. IEEE Trans. Instrum. Meas. 65(6):1423–1434, 2016. https://doi.org/10.1109/TIM.2016.2519779.
Koshti, M., Ganorkar, S., and Chiari, L., IoT based health monitoring system by using raspberry pi and ECG signaly. Int. J. Innov. Res. Sci. Eng. Technol. 5(5):8977–8985, 2016. https://doi.org/10.15680/IJIRSET.2016.0505337.
Menychtas A, et al. On the integration of wearable sensors in IoT enabled mHealth and quantified-self applications. Interactive Mobile communication, technologies and learning. Springer, Cham, 2017. https://doi.org/10.1007/978-3-319-75175-7_9
Healthcare 4.0, https://www.healthcare.siemens.com/magazine/mso-digitalization-healthcare.html
Kalra, M., Lal, N., Data mining of heterogeneous data with research challenges. In IEEE Colossal Data Analysis and Networking (CDAN), 1–6, 2016. https://doi.org/10.1109/CDAN.2016.7570899
Mead, C. N., Data interchange standards in healthcare IT-computable semantic interoperability: Now possible but still difficult, do we really need a better mousetrap? J. Healthc. Inf. Manag. 20(1):71–78, 2006.
HL7 FHIR, https://www.hl7.org/fhir/. Accessed 20 November 2018.
Dumontier, M., Building an effective semantic web for health care and the life sciences. Semantic Web J. 1(1, 2):131–135, 2010. https://doi.org/10.3233/SW-2010-0018.
ETL CHALLENGES WITHIN HEALTHCARE BUSINESS INTELLIGENCE, http://www.amitechsolutions.com/uncategorized/etl-challenges-within-healthcare-business-intelligence/. Accessed 20 November 2018.
3 Ways to Build an ETL process, https://panoply.io/data-warehouse-guide/3-ways-to-build-an-etl-process/. Accessed 20 November 2018.
Márcio Freire, C., Teixeira Cavalcante, C. A. M., and Torres Sá Barretto, S., Using OPC and HL7 standards to incorporate an industrial big data historian in a health IT environment. J. Med. Syst. 42(7):122, 2018. https://doi.org/10.1007/s10916-018-0979-5.
Kasthurirathne, S. N. et al., Enabling better interoperability for healthcare: Lessons in developing a standards based application programing interface for electronic medical record systems. J. Med. Syst. 39(11):182, 2015. https://doi.org/10.1007/s10916-015-0356-6.
Semenov, I. et al., Patients decision aid system based on FHIR profiles. J. Med. Syst. 42(9):166, 2018. https://doi.org/10.1007/s10916-018-1016-4.
Plastiras, P., and O’Sullivan, D. M., Combining ontologies and open standards to derive a middle layer information model for interoperability of personal and electronic health records. J. Med. Syst. 41(12):195, 2017. https://doi.org/10.1007/s10916-017-0838-9.
Transforming data into knowledge relevant to health and disease, https://insights.mdc-berlin.de/en/2016/02/transforming-data-into-knowledge-relevant-to-health-and-disease/. Accessed 20 November 2018.
ORACLE HEALTHCARE ANALYTICS DATA INTEGRATION, http://www.oracle.com/us/industries/healthcare/healthcare-analytics-integration-ds-1360701.pdf. Accessed 20 November 2018.
Dozer, http://dozer.sourceforge.net/. Accessed 20 November 2018.
Language-Integrated Query (LINQ), https://docs.microsoft.com/en-us/dotnet/csharp/programming-guide/concepts/linq/data-transformations-with-linq. Accessed 20 November 2018.
Trang, http://www.thaiopensource.com/relaxng/trang.html. Accessed 20 November 2018.
XML Schema Object Model (XSOM), https://directory.fsf.org/wiki/XSOM. Accessed 20 November 2018.
Jung, http://jung.sourceforge.net/. Accessed 20 November 2018.
Mokhtaria, H., Nait Bahloul, S., and Cruz, C., Transforming XML documents to OWL ontologies: A survey. J. Inf. Sci. 41(2):242–259, 2015. https://doi.org/10.1177/0165551514565972.
Apache Jena, https://jena.apache.org/. Accessed 20 November 2018.
RDF entities, https://www.infowebml.ws/rdf-owl/. Accessed 20 November 2018.
RDF triplestore, https://ontotext.com/knowledgehub/fundamentals/what-is-rdf-triplestore/. Accessed 20 November 2018.
Neter, J., Wasserman, W., and Kutner, M. H., Applied linear regression models. Chicago: Irwin, 1996.
Cortical.io, http://www.cortical.io/technology.html. Accessed 20 November 2018.
Misfit Vapor, https://misfit.com/misfit-vapor. Accessed 20 November 2018.
BioAssist, https://bioassist.gr/. Accessed 20 November 2018.
NetBeans IDE, https://netbeans.org/. Accessed 20 November 2018.
UMLS-based reference alignment, http://www.cs.ox.ac.uk/isg/projects/SEALS/oaei/2017/oaei2017_umls_reference.html. Accessed 20 November 2018.
Kirrane, S., Villata, S., and d’Aquin, M., Privacy, security and policies: A review of problems and solutions with semantic web technologies. Semantic Web J. Preprint 9(2):1–10, 2018. https://doi.org/10.3233/SW-180289.
Kiourtis, A., Mavrogiorgou, A., Kyriazis, D., FHIR Ontology Mapper (FOM): Aggregating Structural and Semantic Similarities of Ontologies towards their Alignment to HL7 FHIR, IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), 1–7, 2018. https://doi.org/10.1109/HealthCom.2018.8531149
Funding
Τhe research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grants (1792, and 2468).
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Kiourtis, A., Mavrogiorgou, A., Menychtas, A. et al. Structurally Mapping Healthcare Data to HL7 FHIR through Ontology Alignment. J Med Syst 43, 62 (2019). https://doi.org/10.1007/s10916-019-1183-y
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DOI: https://doi.org/10.1007/s10916-019-1183-y