Graphical Database Architecture For Clinical Trials

The general area of the research is Health Informatics. The research focuses on creating an innovative and novel solution to manage and analyze clinical trials data. It constructs a Graphical Database Architecture (GDA) for Clinical Trials (CT) using New Technology for Java (Neo4j) as a robust, a scalable and a high-performance database. The purpose of the research project is to develop concepts and techniques based on architecture to accelerate the processing time of clinical data navigation at lower cost. The research design uses a positivist approach to empirical research. The research is significant because it proposes a new approach of clinical trials through graph theory and designs a responsive structure of clinical data that can be deployed across all the health informatics landscape. It uniquely contributes to scholarly literature of the phenomena of Not only SQL (NoSQL) graph databases, mainly Neo4j in CT, for future research of clinical informatics. A prototype is created and examined to validate the concepts, taking advantage of Neo4j’s high availability, scalability, and powerful graph query language (Cypher). This research study finds that integration of search methodologies and information retrieval with the graphical database provides a solid starting point to manage, query, and analyze the clinical trials data, furthermore the design and the development of a prototype demonstrate the conceptual model of this study. Likewise the proposed clinical trials ontology (CTO) incorporates all data elements of a standard clinical study which facilitate a heuristic overview of treatments, interventions, and outcome results of these studies

Using linked data for integrating educational medical web databases based on bioMedical ontologies

Open data are playing a vital role in different communities, including governments, businesses, and education. This revolution has had a high impact on the education field. Recently, Linked Data are being adopted for publishing and connecting data on the web by exposing and connecting data which were not previously linked. In the context of education, applying Linked Data to the growing amount of open data used for learning is potentially highly beneficial. This paper proposes a system that tackles the challenges of data acquisition and integration from distributed web data sources into one linked dataset. The application domain of this work is medical education, and the focus is on integrating educational content in the form of articles published in online educational libraries and Web 2.0 content that can be used for education. The process of integrating a collection of heterogeneous resources is to create links that connect the resources collected from distributed web data sources based on their semantics. The proposed system harvests metadata from distributed web sources and enriches it with concepts from biomedical ontologies, such as SNOMED CT, that enable its linking. The final result of building this system is a linked dataset of more than 10,000 resources collected from PubMed Library, YouTube channels, and Blogging platforms. The final linked dataset is evaluated by developing information retrieval methods that exploit the SNOMED CT hierarchical relations for accessing and querying the dataset. Ontology-based browsing method has been developed for exploring the dataset, and the browsing results have been clustered to evaluate its linkages. Furthermore, ontology-based query searching method has been developed and tested to enhance the discoverability of the data. The results were promising and had shown that using SNOMED CT for integrating distributed resources on the web is beneficial

A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration

This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data. The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the ‘Best Student Paper Award’)

Enriching information extraction pipelines in clinical decision support systems

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumo] Os estudos sanitarios de múltiples centros son importantes para aumentar a repercusión dos resultados da investigación médica debido ao número de suxeitos que poden participar neles. Para simplificar a execución destes estudos, o proceso de intercambio de datos debería ser sinxelo, por exemplo, mediante o uso de bases de datos interoperables. Con todo, a consecución desta interoperabilidade segue sendo un tema de investigación en curso, sobre todo debido aos problemas de gobernanza e privacidade dos datos. Na primeira fase deste traballo, propoñemos varias metodoloxías para optimizar os procesos de estandarización das bases de datos sanitarias. Este traballo centrouse na estandarización de fontes de datos heteroxéneas nun esquema de datos estándar, concretamente o OMOP CDM, que foi desenvolvido e promovido pola comunidade OHDSI. Validamos a nosa proposta utilizando conxuntos de datos de pacientes con enfermidade de Alzheimer procedentes de distintas institucións. Na seguinte etapa, co obxectivo de enriquecer a información almacenada nas bases de datos de OMOP CDM, investigamos solucións para extraer conceptos clínicos de narrativas non estruturadas, utilizando técnicas de recuperación de información e de procesamento da linguaxe natural. A validación realizouse a través de conxuntos de datos proporcionados en desafíos científicos, concretamente no National NLP Clinical Challenges(n2c2). Na etapa final, propuxémonos simplificar a execución de protocolos de estudos provenientes de múltiples centros, propoñendo solucións novas para perfilar, publicar e facilitar o descubrimento de bases de datos. Algunhas das solucións desenvolvidas están a utilizarse actualmente en tres proxectos europeos destinados a crear redes federadas de bases de datos de saúde en toda Europa.[Resumen] Los estudios sanitarios de múltiples centros son importantes para aumentar la repercusión de los resultados de la investigación médica debido al número de sujetos que pueden participar en ellos. Para simplificar la ejecución de estos estudios, el proceso de intercambio de datos debería ser sencillo, por ejemplo, mediante el uso de bases de datos interoperables. Sin embargo, la consecución de esta interoperabilidad sigue siendo un tema de investigación en curso, sobre todo debido a los problemas de gobernanza y privacidad de los datos. En la primera fase de este trabajo, proponemos varias metodologías para optimizar los procesos de estandarización de las bases de datos sanitarias. Este trabajo se centró en la estandarización de fuentes de datos heterogéneas en un esquema de datos estándar, concretamente el OMOP CDM, que ha sido desarrollado y promovido por la comunidad OHDSI. Validamos nuestra propuesta utilizando conjuntos de datos de pacientes con enfermedad de Alzheimer procedentes de distintas instituciones. En la siguiente etapa, con el objetivo de enriquecer la información almacenada en las bases de datos de OMOP CDM, hemos investigado soluciones para extraer conceptos clínicos de narrativas no estructuradas, utilizando técnicas de recuperación de información y de procesamiento del lenguaje natural. La validación se realizó a través de conjuntos de datos proporcionados en desafíos científicos, concretamente en el National NLP Clinical Challenges (n2c2). En la etapa final, nos propusimos simplificar la ejecución de protocolos de estudios provenientes de múltiples centros, proponiendo soluciones novedosas para perfilar, publicar y facilitar el descubrimiento de bases de datos. Algunas de las soluciones desarrolladas se están utilizando actualmente en tres proyectos europeos destinados a crear redes federadas de bases de datos de salud en toda Europa.[Abstract] Multicentre health studies are important to increase the impact of medical research findings due to the number of subjects that they are able to engage. To simplify the execution of these studies, the data-sharing process should be effortless, for instance, through the use of interoperable databases. However, achieving this interoperability is still an ongoing research topic, namely due to data governance and privacy issues. In the first stage of this work, we propose several methodologies to optimise the harmonisation pipelines of health databases. This work was focused on harmonising heterogeneous data sources into a standard data schema, namely the OMOP CDM which has been developed and promoted by the OHDSI community. We validated our proposal using data sets of Alzheimer’s disease patients from distinct institutions. In the following stage, aiming to enrich the information stored in OMOP CDM databases, we have investigated solutions to extract clinical concepts from unstructured narratives, using information retrieval and natural language processing techniques. The validation was performed through datasets provided in scientific challenges, namely in the National NLP Clinical Challenges (n2c2). In the final stage, we aimed to simplify the protocol execution of multicentre studies, by proposing novel solutions for profiling, publishing and facilitating the discovery of databases. Some of the developed solutions are currently being used in three European projects aiming to create federated networks of health databases across Europe

DIN Spec 91345 RAMI 4.0 compliant data pipelining: An approach to support data understanding and data acquisition in smart manufacturing environments

Today, data scientists in the manufacturing domain are confronted with a set of challenges associated to data acquisition as well as data processing including the extraction of valuable in-formation to support both, the work of the manufacturing equipment as well as the manufacturing processes behind it. One essential aspect related to data acquisition is the pipelining, including various commu-nication standards, protocols and technologies to save and transfer heterogenous data. These circumstances make it hard to understand, find, access and extract data from the sources depend-ing on use cases and applications. In order to support this data pipelining process, this thesis proposes the use of the semantic model. The selected semantic model should be able to describe smart manufacturing assets them-selves as well as to access their data along their life-cycle. As a matter of fact, there are many research contributions in smart manufacturing, which already came out with reference architectures or standards for semantic-based meta data descrip-tion or asset classification. This research builds upon these outcomes and introduces a novel se-mantic model-based data pipelining approach using as a basis the Reference Architecture Model for Industry 4.0 (RAMI 4.0).Hoje em dia, os cientistas de dados no domínio da manufatura são confrontados com várias normas, protocolos e tecnologias de comunicação para gravar, processar e transferir vários tipos de dados. Estas circunstâncias tornam difícil compreender, encontrar, aceder e extrair dados necessários para aplicações dependentes de casos de utilização, desde os equipamentos aos respectivos processos de manufatura. Um aspecto essencial poderia ser um processo de canalisação de dados incluindo vários normas de comunicação, protocolos e tecnologias para gravar e transferir dados. Uma solução para suporte deste processo, proposto por esta tese, é a aplicação de um modelo semântico que descreva os próprios recursos de manufactura inteligente e o acesso aos seus dados ao longo do seu ciclo de vida. Muitas das contribuições de investigação em manufatura inteligente já produziram arquitecturas de referência como a RAMI 4.0 ou normas para a descrição semântica de meta dados ou classificação de recursos. Esta investigação baseia-se nestas fontes externas e introduz um novo modelo semântico baseado no Modelo de Arquitectura de Referência para Indústria 4.0 (RAMI 4.0), em conformidade com a abordagem de canalisação de dados no domínio da produção inteligente como caso exemplar de utilização para permitir uma fácil exploração, compreensão, descoberta, selecção e extracção de dados

ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research

Conceptual Modeling Applied to Genomics: Challenges Faced in Data Loading

Todays genomic domain evolves around insecurity: too many imprecise concepts, too much information to be properly managed. Considering that conceptualization is the most exclusive human characteristic, it makes full sense to try to conceptualize the principles that guide the essence of why humans are as we are. This question can of course be generalized to any species, but we are especially interested in this work in showing how conceptual modeling is strictly required to understand the ''execution model'' that human beings ''implement''. The main issue is to defend the idea that only by having an in-depth knowledge of the Conceptual Model that is associated to the Human Genome, can this Human Genome properly be understood. This kind of Model-Driven perspective of the Human Genome opens challenging possibilities, by looking at the individuals as implementation of that Conceptual Model, where different values associated to different modeling primitives will explain the diversity among individuals and the potential, unexpected variations together with their unwanted effects in terms of illnesses. This work focuses on the challenges faced in loading data from conventional resources into Information Systems created according to the above mentioned conceptual modeling approach. The work reports on various loading efforts, problems encountered and the solutions to these problems. Also, a strong argument is made about why conventional methods to solve the so called `data chaos¿ problems associated to the genomics domain so often fail to meet the demands.Van Der Kroon ., M. (2011). Conceptual Modeling Applied to Genomics: Challenges Faced in Data Loading. http://hdl.handle.net/10251/16993Archivo delegad