The Management and Integration of Biomedical Knowledge: Application in the Health-e-Child Project (Position Paper)

Jiménez-Ruiz, Ernesto; Berlanga, Rafael; Sanz, Ismael; McClatchey, Richard; Dánger, Roxana; Manset, David; Paraire, J.; Rios, A.

doi:10.1007/11915072_8

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The work presented in this paper has been mainly developed in the context of the European Health-e-Child (HeC) integrated project [ 31 , 32 ]. HeC aimed to develop an integrated health care platform to allow clinicians to access, analyze, evaluate, enhance and exchange integrated biomedical information focused on three pediatric domains: heart disorders, inflammatory disorders and brain tumors.…”

Section: Resultsmentioning

confidence: 99%

Exploring and linking biomedical resources through multidimensional semantic spaces

2012

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BackgroundThe semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes).ResultsThis paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource.ConclusionsCurrent knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces.

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Section: Resultsmentioning

confidence: 99%

Exploring and linking biomedical resources through multidimensional semantic spaces

2012

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“…Neste contexto, a área da Saúde apresenta inúmeras tarefas e aplicações que podem ser realizadas por meio de sistemas inteligentes, desde a utilização da tecnologia para auxiliar no gerenciamento e processamento de dados clínicos em simples planilhas informatizadas, até a criação de um modelo robusto de gerenciamento desses dados [1][2][3][4][5][6][7].…”

Section: Introductionunclassified

Biomedical Data Management and Processing -A New Framework

Ribeiro

Albuquerque

Fernandes

et al. 2015

IEEE Latin Am. Trans.

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Abstract-The integration of information from biomedical devices is of fundamental importance for effective medical diagnosis. In this sense, the present work aimed to develop a new framework able to manage and process biomedical data in real time. The major advantage of the proposed solution is the ability to add new medical devices and integrate their results with the existing ones. The devices tested include brainwave sensors, a baropodometric treadmill and a biomedical kit composed of a patient position sensor (accelerometer), glucometer, body temperature, blood pressure, pulse and oxygen in blood, airflow, galvanic skin response and electrocardiogram sensors. From the tests conducted, it can be concluded that the proposed framework is robust, accurate and fast, and can manage and process large volumes of data in real time. Customizable graphs can be built from the electroencephalogramsignals acquired during patient gait, which can be analyzed based on barographic image registration. Finally, it can be concluded that the framework is quite promising to be used to assist medical diagnosis and improve and accelerate the treatment of patients.

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