The Experts below are selected from a list of 171 Experts worldwide ranked by ideXlab platform
Montserrat Robles - One of the best experts on this subject based on the ideXlab platform.
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AMIA - A platform for exploration into chaining of web services for clinical data transFormation and reasoning.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017Co-Authors: Jose Alberto Maldonado, Mar Marcos, Begoña Martínez-salvador, Jesualdo Tomás Fernández-breis, Estibaliz Parcero, Diego Boscá, María Del Carmen Legaz-garcía, Montserrat RoblesAbstract:The heterogeneity of clinical data is a key problem in the sharing and reuse of Electronic Health Record (EHR) data. We approach this problem through the combined use of EHR standards and semantic web technologies, concretely by means of clinical data transFormation applications that convert EHR data in Proprietary Format, first into clinical inFormation models based on archetypes, and then into RDF/OWL extracts which can be used for automated reasoning. In this paper we describe a proof-of-concept platform to facilitate the (re)configuration of such clinical data transFormation applications. The platform is built upon a number of web services dealing with transFormations at different levels (such as normalization or abstraction), and relies on a collection of reusable mappings designed to solve specific transFormation steps in a particular clinical domain. The platform has been used in the development of two different data transFormation applications in the area of colorectal cancer.
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leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts
Journal of the American Medical Informatics Association, 2013Co-Authors: Jesualdo Tomas Fernandezbreis, Mar Marcos, Maria Del Carmen Legazgarcia, Joaquin Torressospedra, Begona Martinezsalvador, Angel Estebangil, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract:Background 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. Objective 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 (ie, data level) and the rest using ontologies (ie, 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusions 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.
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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.
Journal of the American Medical Informatics Association : JAMIA, 2013Co-Authors: Jesualdo Tomás Fernández-breis, Mar Marcos, María Del Carmen Legaz-garcía, Joaquín Torres-sospedra, Begoña Martínez-salvador, Angel Esteban, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract: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. 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. 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 (ie, data level) and the rest using ontologies (ie, 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. 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. 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.
Mar Marcos - One of the best experts on this subject based on the ideXlab platform.
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AMIA - A platform for exploration into chaining of web services for clinical data transFormation and reasoning.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017Co-Authors: Jose Alberto Maldonado, Mar Marcos, Begoña Martínez-salvador, Jesualdo Tomás Fernández-breis, Estibaliz Parcero, Diego Boscá, María Del Carmen Legaz-garcía, Montserrat RoblesAbstract:The heterogeneity of clinical data is a key problem in the sharing and reuse of Electronic Health Record (EHR) data. We approach this problem through the combined use of EHR standards and semantic web technologies, concretely by means of clinical data transFormation applications that convert EHR data in Proprietary Format, first into clinical inFormation models based on archetypes, and then into RDF/OWL extracts which can be used for automated reasoning. In this paper we describe a proof-of-concept platform to facilitate the (re)configuration of such clinical data transFormation applications. The platform is built upon a number of web services dealing with transFormations at different levels (such as normalization or abstraction), and relies on a collection of reusable mappings designed to solve specific transFormation steps in a particular clinical domain. The platform has been used in the development of two different data transFormation applications in the area of colorectal cancer.
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leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts
Journal of the American Medical Informatics Association, 2013Co-Authors: Jesualdo Tomas Fernandezbreis, Mar Marcos, Maria Del Carmen Legazgarcia, Joaquin Torressospedra, Begona Martinezsalvador, Angel Estebangil, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract:Background 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. Objective 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 (ie, data level) and the rest using ontologies (ie, 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusions 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.
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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.
Journal of the American Medical Informatics Association : JAMIA, 2013Co-Authors: Jesualdo Tomás Fernández-breis, Mar Marcos, María Del Carmen Legaz-garcía, Joaquín Torres-sospedra, Begoña Martínez-salvador, Angel Esteban, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract: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. 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. 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 (ie, data level) and the rest using ontologies (ie, 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. 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. 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.
Jose Alberto Maldonado - One of the best experts on this subject based on the ideXlab platform.
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AMIA - A platform for exploration into chaining of web services for clinical data transFormation and reasoning.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017Co-Authors: Jose Alberto Maldonado, Mar Marcos, Begoña Martínez-salvador, Jesualdo Tomás Fernández-breis, Estibaliz Parcero, Diego Boscá, María Del Carmen Legaz-garcía, Montserrat RoblesAbstract:The heterogeneity of clinical data is a key problem in the sharing and reuse of Electronic Health Record (EHR) data. We approach this problem through the combined use of EHR standards and semantic web technologies, concretely by means of clinical data transFormation applications that convert EHR data in Proprietary Format, first into clinical inFormation models based on archetypes, and then into RDF/OWL extracts which can be used for automated reasoning. In this paper we describe a proof-of-concept platform to facilitate the (re)configuration of such clinical data transFormation applications. The platform is built upon a number of web services dealing with transFormations at different levels (such as normalization or abstraction), and relies on a collection of reusable mappings designed to solve specific transFormation steps in a particular clinical domain. The platform has been used in the development of two different data transFormation applications in the area of colorectal cancer.
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leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts
Journal of the American Medical Informatics Association, 2013Co-Authors: Jesualdo Tomas Fernandezbreis, Mar Marcos, Maria Del Carmen Legazgarcia, Joaquin Torressospedra, Begona Martinezsalvador, Angel Estebangil, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract:Background 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. Objective 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 (ie, data level) and the rest using ontologies (ie, 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusions 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.
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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.
Journal of the American Medical Informatics Association : JAMIA, 2013Co-Authors: Jesualdo Tomás Fernández-breis, Mar Marcos, María Del Carmen Legaz-garcía, Joaquín Torres-sospedra, Begoña Martínez-salvador, Angel Esteban, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract: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. 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. 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 (ie, data level) and the rest using ontologies (ie, 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. 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. 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.
David Moner - One of the best experts on this subject based on the ideXlab platform.
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leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts
Journal of the American Medical Informatics Association, 2013Co-Authors: Jesualdo Tomas Fernandezbreis, Mar Marcos, Maria Del Carmen Legazgarcia, Joaquin Torressospedra, Begona Martinezsalvador, Angel Estebangil, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract:Background 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. Objective 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 (ie, data level) and the rest using ontologies (ie, 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusions 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.
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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.
Journal of the American Medical Informatics Association : JAMIA, 2013Co-Authors: Jesualdo Tomás Fernández-breis, Mar Marcos, María Del Carmen Legaz-garcía, Joaquín Torres-sospedra, Begoña Martínez-salvador, Angel Esteban, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract: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. 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. 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 (ie, data level) and the rest using ontologies (ie, 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. 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. 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.
Jesualdo Tomás Fernández-breis - One of the best experts on this subject based on the ideXlab platform.
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AMIA - A platform for exploration into chaining of web services for clinical data transFormation and reasoning.
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017Co-Authors: Jose Alberto Maldonado, Mar Marcos, Begoña Martínez-salvador, Jesualdo Tomás Fernández-breis, Estibaliz Parcero, Diego Boscá, María Del Carmen Legaz-garcía, Montserrat RoblesAbstract:The heterogeneity of clinical data is a key problem in the sharing and reuse of Electronic Health Record (EHR) data. We approach this problem through the combined use of EHR standards and semantic web technologies, concretely by means of clinical data transFormation applications that convert EHR data in Proprietary Format, first into clinical inFormation models based on archetypes, and then into RDF/OWL extracts which can be used for automated reasoning. In this paper we describe a proof-of-concept platform to facilitate the (re)configuration of such clinical data transFormation applications. The platform is built upon a number of web services dealing with transFormations at different levels (such as normalization or abstraction), and relies on a collection of reusable mappings designed to solve specific transFormation steps in a particular clinical domain. The platform has been used in the development of two different data transFormation applications in the area of colorectal cancer.
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Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts.
Journal of the American Medical Informatics Association : JAMIA, 2013Co-Authors: Jesualdo Tomás Fernández-breis, Mar Marcos, María Del Carmen Legaz-garcía, Joaquín Torres-sospedra, Begoña Martínez-salvador, Angel Esteban, Jose Alberto Maldonado, David Moner, Montserrat RoblesAbstract: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. 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. 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 (ie, data level) and the rest using ontologies (ie, 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. 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 have been used and the patients have been successfully classified by the risk of developing colorectal cancer. 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.
