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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts

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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts

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dc.contributor.author Fernández-Breis, Jesualdo Tomás es_ES
dc.contributor.author Maldonado Segura, José Alberto es_ES
dc.contributor.author Marcos, Marc es_ES
dc.contributor.author Legaz-García, María del Carmen es_ES
dc.contributor.author Moner Cano, David es_ES
dc.contributor.author Torres-Sospedra, Joaquín es_ES
dc.contributor.author Esteban-Gil, Angel es_ES
dc.contributor.author Martínez-Salvador, Begoña es_ES
dc.contributor.author Robles Viejo, Montserrat es_ES
dc.date.accessioned 2014-11-18T11:36:25Z
dc.date.available 2014-11-18T11:36:25Z
dc.date.issued 2013-12
dc.identifier.issn 1067-5027
dc.identifier.uri http://hdl.handle.net/10251/44370
dc.description.abstract Introduction The secondary use of Electronic Healthcare Records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, Virtual Health Records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. Our main objective is to develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. Materials and Methods We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (i.e., data level) and the rest using ontologies (i.e., knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. Results We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data has been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusion This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies and classification rules can be designed. es_ES
dc.description.sponsorship This work was supported by the Ministerio de Economia y Competitividad and the FEDER program through grants TIN2010-21388-C01 and TIN2010-21388-C02. MCLG was supported by the Fundacion Seneca through grant 15555/FPI/2010. en_EN
dc.language Inglés es_ES
dc.publisher BMJ Publishing Group es_ES
dc.relation Ministerio de Economia y Competitividad es_ES
dc.relation FEDER TIN2010-21388-C01 TIN2010-21388-C02 es_ES
dc.relation Fundacion Seneca 15555/FPI/2010 es_ES
dc.relation.ispartof Journal of the American Medical Informatics Association es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Medical Informatics es_ES
dc.subject Electronic Health Records/standards es_ES
dc.subject Semantics es_ES
dc.subject Decision Support Systems es_ES
dc.subject Clinical es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1136/amiajnl-2013-001923
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Fernández-Breis, JT.; Maldonado Segura, JA.; Marcos, M.; Legaz-García, MDC.; Moner Cano, D.; Torres-Sospedra, J.; Esteban-Gil, A.... (2013). Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts. Journal of the American Medical Informatics Association. 20(E2):288-296. doi:10.1136/amiajnl-2013-001923 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1136/amiajnl-2013-001923 es_ES
dc.description.upvformatpinicio 288 es_ES
dc.description.upvformatpfin 296 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue E2 es_ES
dc.relation.senia 252212
dc.identifier.pmcid PMC3861938 en_EN
dc.description.references Cuggia, M., Besana, P., & Glasspool, D. (2011). Comparing semi-automatic systems for recruitment of patients to clinical trials. International Journal of Medical Informatics, 80(6), 371-388. doi:10.1016/j.ijmedinf.2011.02.003 es_ES
dc.description.references Sujansky, W. (2001). Heterogeneous Database Integration in Biomedicine. Journal of Biomedical Informatics, 34(4), 285-298. doi:10.1006/jbin.2001.1024 es_ES
dc.description.references Schadow G Russler DC Mead CN . Integrating medical information and knowledge in the HL7 RIM. Proceedings of the AMIA Symposium, 2000:764–8. es_ES
dc.description.references Johnson PD Tu SW Musen MA . A virtual medical record for guideline-based decision support. Proceedings of the AMIA 2001 Annual Symposium, 294–8. es_ES
dc.description.references German, E., Leibowitz, A., & Shahar, Y. (2009). An architecture for linking medical decision-support applications to clinical databases and its evaluation. Journal of Biomedical Informatics, 42(2), 203-218. doi:10.1016/j.jbi.2008.10.007 es_ES
dc.description.references Peleg, M., Keren, S., & Denekamp, Y. (2008). Mapping computerized clinical guidelines to electronic medical records: Knowledge-data ontological mapper (KDOM). Journal of Biomedical Informatics, 41(1), 180-201. doi:10.1016/j.jbi.2007.05.003 es_ES
dc.description.references Maldonado, J. A., Costa, C. M., Moner, D., Menárguez-Tortosa, M., Boscá, D., Miñarro Giménez, J. A., … Robles, M. (2012). Using the ResearchEHR platform to facilitate the practical application of the EHR standards. Journal of Biomedical Informatics, 45(4), 746-762. doi:10.1016/j.jbi.2011.11.004 es_ES
dc.description.references Parker CG Rocha RA Campbell JR . Detailed clinical models for sharable, executable guidelines. Stud Health Technol Inform 2004;107:145–8. es_ES
dc.description.references Clinical Information Modeling Initiative. http://informatics.mayo.edu/CIMI/index.php/Main_Page (accessed Jun 2013). es_ES
dc.description.references W3C, OWL2 Web Ontology Language. http://www.w3.org/TR/owl2-overview/ (accessed Jun 2013). es_ES
dc.description.references European Commission. Semantic interoperability for better health and safer healthcare. Deployment and research roadmap for Europe. ISBN-13: 978-92-79-11139-6, 2009. es_ES
dc.description.references SemanticHealthNet. http://www.semantichealthnet.eu/ (accessed Jun 2013). es_ES
dc.description.references Martínez-Costa, C., Menárguez-Tortosa, M., Fernández-Breis, J. T., & Maldonado, J. A. (2009). A model-driven approach for representing clinical archetypes for Semantic Web environments. Journal of Biomedical Informatics, 42(1), 150-164. doi:10.1016/j.jbi.2008.05.005 es_ES
dc.description.references Iqbal AM . An OWL-DL ontology for the HL7 reference information model. Toward useful services for elderly and people with disabilities Berlin: Springer, 2011:168–75. es_ES
dc.description.references Tao, C., Jiang, G., Oniki, T. A., Freimuth, R. R., Zhu, Q., Sharma, D., … Chute, C. G. (2012). A semantic-web oriented representation of the clinical element model for secondary use of electronic health records data. Journal of the American Medical Informatics Association, 20(3), 554-562. doi:10.1136/amiajnl-2012-001326 es_ES
dc.description.references Heymans, S., McKennirey, M., & Phillips, J. (2011). Semantic validation of the use of SNOMED CT in HL7 clinical documents. Journal of Biomedical Semantics, 2(1), 2. doi:10.1186/2041-1480-2-2 es_ES
dc.description.references Menárguez-Tortosa, M., & Fernández-Breis, J. T. (2013). OWL-based reasoning methods for validating archetypes. Journal of Biomedical Informatics, 46(2), 304-317. doi:10.1016/j.jbi.2012.11.009 es_ES
dc.description.references Lezcano, L., Sicilia, M.-A., & Rodríguez-Solano, C. (2011). Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules. Journal of Biomedical Informatics, 44(2), 343-353. doi:10.1016/j.jbi.2010.11.005 es_ES
dc.description.references Tao C Wongsuphasawat K Clark K . Towards event sequence representation, reasoning and visualization for EHR data. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI'12). New York, NY, USA: ACM:801–6. es_ES
dc.description.references Bodenreider O . Biomedical ontologies in action: role in knowledge management, data integration and decision support. IMIA Yearbook of Medical Informatics 2008;67–79. es_ES
dc.description.references Beale T . Archetypes. Constraint-based domain models for future-proof information systems. http://www.openehr.org/files/publications/archetypes/archetypes_beale_web_2000.pdf es_ES
dc.description.references SNOMED-CT. http://www.ihtsdo.org/snomed-ct/ (accessed Jun 2013). es_ES
dc.description.references UMLS Terminology Services. https://uts.nlm.nih.gov/home.html (accessed Jun 2013). es_ES
dc.description.references The openEHR Foundation, openEHR Clinical Knowledge Manager. http://www.openehr.org/knowledge/ (accessed Jun 2013). es_ES
dc.description.references Maldonado, J. A., Moner, D., Boscá, D., Fernández-Breis, J. T., Angulo, C., & Robles, M. (2009). LinkEHR-Ed: A multi-reference model archetype editor based on formal semantics. International Journal of Medical Informatics, 78(8), 559-570. doi:10.1016/j.ijmedinf.2009.03.006 es_ES
dc.description.references SAXON XSLT and XQuery processor. http://saxon.sourceforge.net/ (accessed Jun 2013). es_ES
dc.description.references NCBO Bioportal. http://bioportal.bioontology.org/ (accessed Jun 2013). es_ES
dc.description.references The Protégé Ontology Editor and Knowledge Acquisition System. http://protege.stanford.edu/ (accessed Jun 2013). es_ES
dc.description.references Semantic Web Integration Tool. http://sele.inf.um.es/swit (accessed Jun 2013). es_ES
dc.description.references Hermit Reasoner. http://www.hermit-reasoner.com/ (accessed Jun 2013). es_ES
dc.description.references The OWLAPI. http://owlapi.sourceforge.net/ (accessed Jun 2013). es_ES
dc.description.references Institute for Health Metrics and Evaluation. Global Burden of Disease. http://www.healthmetricsandevaluation.org/gbd (accessed Jun 2013). es_ES
dc.description.references Segnan N Patnick J von Karsa L . European guidelines for quality assurance in colorectal cancer screening and diagnosis 2010. First Edition. European Union. ISBN 978-92-79-16435-4. es_ES
dc.description.references W3C. XQuery 1.0: An XML Query Language. http://www.w3.org/TR/xquery/ (accessed Jun 2013). es_ES
dc.description.references DL Query. http://protegewiki.stanford.edu/wiki/DL_Query (accessed Jun 2013). es_ES
dc.description.references SPARQL Query Language for RDF. http://www.w3.org/TR/rdf-sparql-query/ (accessed Jun 2013). es_ES
dc.description.references Semantic Web Rule Language. http://www.w3.org/Submission/SWRL/ (accessed Jun 2013). es_ES
dc.description.references Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Boscá, D., & Robles, M. (2013). Interoperability of clinical decision-support systems and electronic health records using archetypes: A case study in clinical trial eligibility. Journal of Biomedical Informatics, 46(4), 676-689. doi:10.1016/j.jbi.2013.05.004 es_ES
dc.description.references Marcos, M., Maldonado, J. A., Martínez-Salvador, B., Moner, D., Boscá, D., & Robles, M. (2011). An Archetype-Based Solution for the Interoperability of Computerised Guidelines and Electronic Health Records. Lecture Notes in Computer Science, 276-285. doi:10.1007/978-3-642-22218-4_35 es_ES
dc.description.references MobiGuide: Guiding patients anytime everywhere. http://www.mobiguide-project.eu/ (accessed Jun 2013). es_ES
dc.description.references EURECA: Enabling information re-Use by linking clinical RE search and Care. http://eurecaproject.eu/ (accessed Jun 2013). es_ES
dc.description.references Rea, S., Pathak, J., Savova, G., Oniki, T. A., Westberg, L., Beebe, C. E., … Chute, C. G. (2012). Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project. Journal of Biomedical Informatics, 45(4), 763-771. doi:10.1016/j.jbi.2012.01.009 es_ES
dc.description.references Clinical Element Models. http://informatics.mayo.edu/sharp/index.php/CEMS (accessed Jun 2013). es_ES


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