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

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.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.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. https://doi.org/10.1136/amiajnl-2013-001923S28829620E2Cuggia, 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.2011.02.003Sujansky, W. 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Enhanced working memory binding by direct electrical stimulation of the parietal cortex

Recent works evince the critical role of visual short-term memory (STM) binding deficits as a clinical and preclinical marker of Alzheimer's disease (AD). These studies suggest a potential role of posterior brain regions in both the neurocognitive deficits of Alzheimer's patients and STM binding in general. Thereupon, we surmised that stimulation of the posterior parietal cortex (PPC) might be a successful approach to tackle working memory deficits in this condition, especially at early stages. To date, no causal evidence exists of the role of the parietal cortex in STM binding. A unique approach to assess this issue is afforded by single-subject direct intracranial electrical stimulation of specific brain regions during a relevant cognitive task. Electrical stimulation has been used both for clinical purposes and to causally probe brain mechanisms. Previous evidence of electrical currents spreading through white matter along well defined functional circuits indicates that visual working memory mechanisms are subserved by a specific widely distributed network. Here, we stimulated the parietal cortex of a subject with intracranial electrodes as he performed the visual STM task. We compared the ensuing results to those from a non-stimulated condition and to the performance of a matched control group. In brief, direct stimulation of the parietal cortex induced a selective improvement in STM. These results, together with previous studies, provide very preliminary but promising ground to examine behavioral changes upon parietal stimulation in AD. We discuss our results regarding: (a) the usefulness of the task to target prodromal stages of AD; (b) the role of a posterior network in STM binding and in AD; and (c) the potential opportunity to improve STM binding through brain stimulation.Fil: Birba, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: Hesse Rizzi, Eugenia Fátima. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Sedeño, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: Mikulan, Ezequiel Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: García, María del Carmen. Hospital Italiano; ArgentinaFil: Avalos, Juan Carlos. Hospital Italiano; ArgentinaFil: Gonzalez Adolfi, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: Legaz, Agustina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; ArgentinaFil: Bekinschtein, Tristán Andrés. University of Cambridge; Reino UnidoFil: Zimerman, Máximo. Instituto de Neurología Cognitiva; Argentina. Universidad Favaloro; ArgentinaFil: Parra, Mario. Heriot-Watt University; Reino Unido. University of Edinburgh; Reino Unido. NHS Research Scotland; Reino Unido. Universidad Autónoma del Caribe; ColombiaFil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Nacional de Cuyo; ArgentinaFil: Ibáñez Barassi, Agustín Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Autónoma del Caribe; Colombia. Universidad Adolfo Ibañez; Chile. Australian Research Council ; Australi

Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks : The GR@ACE project

Introduction: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. Methods: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. Results: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. Discussion: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series

Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues

Integración de información biomédica basada en tecnologías semánticas avanzadas

Objetivos La medicina traslacional requiere la explotación integrada de información biomédica para dar soporte a la investigación, sin embargo, resulta difícil el acceso al conocimiento biomédico, por ser heterogéneo y estar distribuido entre distintos sistemas de información. El objetivo principal de esta tesis es la investigación y desarrollo de soluciones basadas en las tecnologías de la Web Semántica para la integración de conocimiento biomédico utilizado en medicina traslacional. Metodología Para conseguir este objetivo, primero se lleva a cabo un estudio del estado del arte que incluye los formatos de representación más comunes de la información biomédica, las tecnologías de la Web Semántica y su aplicación en el ámbito biomédico, las propuestas de transformación de contenidos a representación semántica y los sistemas de integración de repositorios heterogéneos. Después, la solución propuesta se formaliza en tres pasos: (1) formalización de una metodología de transformación genérica de información, (2) formalización de un proceso de integración de recursos heterogéneos basado en transformación a una ontología OWL, (3) formalización de una plataforma de integración, gestión y explotación de información biomédica. Todas estas soluciones se implementan en herramientas web. Por último, las soluciones propuestas se validan en cuatro escenarios diferentes: clasificación automática de pacientes a partir de sus datos clínicos dentro un programa de cribado de cáncer de colon y recto; transformación entre modelos clínicos CEM y arquetipos openEHR; creación de un repositorio integrado de genes ortólogos, enfermedades y anotaciones sobre secuencias genómicas; representación OWL de bases de datos de componentes químicos. Resultados Como resultado se obtiene: • Un modelo de transformación genérica de datos. Está compuesto de reglas de correspondencia que permiten la transformación de instancias de entrada a una representación según un modelo de salida, de reglas de identidad que identifican instancias redundantes, y que usa patrones de diseño para realizar transformaciones más complejas. • Un modelo de integración de información biomédica heterogénea, que aplica el modelo de transformación y tiene como modelo de salida una arquitectura ontológica (ontología OWL y patrones de diseño de contenido ontológico) para la transformación e integración de recursos heterogéneos en un repositorio único. • Una plataforma de integración, gestión y explotación de información biomédica, que explota distintas representaciones OWL de modelos clínicos e incluye métodos de validación, anotación, comparación, y búsqueda semántica, además de permitir la ejecución de procesos de transformación e integración. • Dos herramientas web que implementación las soluciones. SWIT realiza la transformación de información a representación RDF/OWL, mientras que ArchMS implementa la plataforma integrada que permite la gestión y explotación de modelos y datos clínicos y su explotación en repositorios semánticos junto a otros recursos biomédicos externos. Conclusiones Los estándares de información clínica tratan de favorecer la interoperabilidad semántica de la información, mientras que propuestas como Linked Open Data fomentan la publicación y enlazado de los datos biomédicos. Sin embargo, los lenguajes utilizados para representar modelos clínicos resultan insuficientes para su gestión, mientras que la mayoría de métodos de publicación de datos en la Web de Datos no tienen en cuenta la semántica del contenido y son difíciles de generalizar. Utilizar modelos globales basados en ontologías OWL en la transformación e integración de contenidos permite definir una transformación dirigida por la semántica del dominio y utilizar esta semántica para explotar el repositorio final. OWL permite validar y comparar el contenido atendiendo a su semántica y facilita la integración de recursos. Las herramientas desarrolladas han demostrado ser efectivas en su utilización en distintos escenarios de validación, creando repositorios semánticos abiertos que contribuyen al desarrollo de la Web de Datos y permitiendo su explotación en el espacio tecnológico de la Web Semántica. Summary Aims of the thesis Translational medicine requires intensive collaboration between different areas of biomedical informatics. However, this collaboration is difficult due to the fact that the biomedical knowledge generated by the different disciplines has the quality of being distributed and heterogeneous. This thesis aims to assist translational research by improving the integrated exploitation of biomedical information through the use of Semantic Web technologies. Methodology The methodology proposed is based on the analysis of the state of art, the formalization of the proposed methods, their implementation and their validation in application domains. The analysis of the state of art includes the study of the most common representation formats for biomedical information, the application of Semantic Web technologies to the biomedical domain, methods of content transformation to semantic representation and existing proposals for integrating heterogeneous repositories. The proposed solution is formalized in three steps: (1) formalization of a generic methodology for semantic data transformation, (2) formalization of a heterogeneous resources integration process based on the transformation into an OWL ontology, (3) formalization of an integrated platform for managing and exploiting the biomedical information. All these proposed solutions are implemented in web tools. The solutions have been validated in four different scenarios: study of clinical data from patients of a colorectal screening program for performing automatic classification of the patients; transformation between CEM clinical models and openEHR archetypes; creation of an integrated repository about orthologous genes, genetic disorders and information about genomic sequences annotations; transformation of a dataset of chemical components into an OWL representation. Results The main contributions of this work are: • A generic data transformation model between structured representation schemata. The definition of mappings transforms input instances into a representation guided by the output model. Identity rules identify redundant instances. The accepted input and output models are defined by a metamodel and the use of design pattern allows making more complex transformations. • A heterogeneous biomedical information integration model. Through the instantiation of the transformation model with an output model defined by an ontological architecture (OWL ontology and ontology design content patterns), different heterogeneous resources are transformed and integrated. • A platform for integrating, managing and exploiting biomedical information. The platform selects the most suitable OWL representations for clinical models and includes semantic methods for validating, annotating, comparing and searching together with the defined transformation and integration models. • Implementation of the transformation and integration models, and the integrated platform in two web applications. SWIT implements the transformation model while AchMS implements the integrated platform. Conclusions Al the clinical level, Electronic Health Record standards intend the achievement of semantic interoperability, while initiatives like Linked Open Data pursues the publication and sharing of biomedical datasets. However, the syntactic nature of languages used for clinical models representation is not enough for their management, while methods for datasets publication in the Web of Data make a syntactic transformation, guided by the logical schema of the source representation and there exists problems in the generalization of the methods. The use of global models based on OWL ontologies for representing information content allows the definition of transformation processes driven by the domain semantics, which can be exploited in the final repository. An OWL representation allows the validation and comparison of the content attending to its semantic, making easier the integration of different resources. The developed tools have demonstrated their effectiveness in different validation scenarios, creating semantic open datasets that will contribute to the development of the Web of Data and allowing their exploitation in the Semantic Web technological space

CLIN-IK-LINKS: A platform for the design and execution of clinical data transformation and reasoning workflows

Background and Objective: Effective sharing and reuse of Electronic Health Records (EHR) requires technological solutions which deal with different representations and different models of data. This includes information models, domain models and, ideally, inference models, which enable clinical decision support based on a knowledge base and facts. Our goal is to develop a framework to support EHR interoperability based on transformation and reasoning services intended for clinical data and knowledge. Methods: Our framework is based on workflows whose primary components are reusable mappings. Key features are an integrated representation, storage, and exploitation of different types of mappings for clinical data transformation purposes, as well as the support for the discovery of new workflows. The current framework supports mappings which take advantage of the best features of EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Results: We have implemented CLIN-IK-LINKS, a web-based platform that enables users to create, modify and delete mappings as well as to define and execute workflows. The platform has been applied in two use cases: semantic publishing of clinical laboratory test results; and implementation of two colorectal cancer screening protocols. Real data have been used in both use cases. Conclusions: The CLIN-IK-LINKS platform allows the composition and execution of clinical data transformation workflows to convert EHR data into EHR and/or semantic web standards. Having proved its usefulness to implement clinical data transformation applications of interest, CLIN-IK-LINKS can be regarded as a valuable contribution to improve the semantic interoperability of EHR systems