The Experts below are selected from a list of 345063 Experts worldwide ranked by ideXlab platform
Michael Stürzl - One of the best experts on this subject based on the ideXlab platform.
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ketos clinical decision support and machine learning as a service a training and deployment platform based on docker omop cdm and fhir web services
2019Co-Authors: Julian Gruendner, Thorsten Schwachhofer, Phillip Sippl, Nicolas Wolf, Marcel Erpenbeck, Christian Gulden, Lorenz A Kapsner, Sebastian Mate, Jakob Zierk, Michael StürzlAbstract:Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end User Application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end User Application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).
Dimitra Simeonidou - One of the best experts on this subject based on the ideXlab platform.
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software defined optical networks technology and infrastructure enabling software defined optical network operations invited
2013Co-Authors: Mayur Channegowda, Reza Nejabati, Dimitra SimeonidouAbstract:Software-defined networking (SDN) enables programmable SDN control and management functions at a number of layers, allowing Applications to control network resources or information across different technology domains, e.g., Ethernet, wireless, and optical. Current cloud-based services are pushing networks to new boundaries by deploying cutting edge optical technologies to provide scalable and flexible services. SDN combined with the latest optical transport technologies, such as elastic optical networks, enables network operators and cloud service providers to customize their infrastructure dynamically to User/Application requirements and therefore minimize the extra capital and operational costs required for hosting new services. In this paper a unified control plane architecture based on OpenFlow for optical SDN tailored to cloud services is introduced. Requirements for its implementation are discussed considering emerging optical transport technologies. Implementations of the architecture are proposed and demonstrated across heterogeneous state-of-the-art optical, packet, and IT resource integrated cloud infrastructure. Finally, its performance is evaluated using cloud use cases and its results are discussed.
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the geysers concept and major outcomes
2013Co-Authors: Anna Tzanakaki, Dimitra Simeonidou, Nicola Ciulli, Sergi Figuerola, Joan A Garciaespin, Philip Robinson, Juan Rodriguez, Giada Landi, Bartosz Belter, Pascale VicatblancAbstract:Large-scale computer networks supporting both communication and computation are extensively employed to deal with a variety of existing and emerging demanding Applications. These high-performance Applications, requiring very high network capacities and specific IT resources, cannot be delivered by the current Best Effort Internet. Optical networking is offering a very high capacity transport with increased dynamicity and flexibility through recent technology advancements including dynamic control planes etc. The European project GEYSERS (Generalised Architecture for Dynamic Infrastructure Services) proposed a novel architecture capable of provisioning “Optical Network and IT resources” for end-to-end service delivery. The proposed approach adopts the Infrastructure as a Service (IaaS) paradigm. The GEYSERS architecture presents an innovative solution to enable infrastructure operators to virtualize their optical network + IT physical resources and offer them as a service based on the User/Application requirements. The adoption of Virtual Infrastructures (VIs) facilitates sharing of physical resources among various virtual operators, introducing new business models that suit well the nature and characteristics of the Future Internet and enables new exploitation opportunities for the underlying Physical Infrastructures (PIs).
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demonstration of low latency intra inter data centre heterogeneous optical sub wavelength network using extended gmpls pce control plane
2013Co-Authors: Bijan Rahimzadeh Rofoee, John Levins, Mark Basham, Georgios Zervas, Giacomo Bernini, Gino Carrozzo, Dimitra Simeonidou, Nicola Ciulli, Yan Yan, John DunneAbstract:This paper reports on the first User/Application-driven multi-technology optical sub-wavelength network for intra/inter Data-Centre (DC) communications. Two DCs each with distinct sub-wavelength switching technologies, frame based synchronous TSON and packet based asynchronous OPST are interconnected by a WSON inter-DC communication. The intra/inter DC testbed demonstrates ultra-low latency (packet-delay <270µs and packet-delay-variation (PDV)<10µs) flexible data-rate traffic transfer by point-to-point, point-to-multipoint, and multipoint-to-(multi)point connectivity, highly suitable for cloud based Applications and high performance computing (HPC). The extended GMPLS-PCE-SLAE based control-plane enables innovative Application-driven end-to-end sub-wavelength path setup and resource reservation across the multi technology data-plane, which has been assessed for as many as 25 concurrent requests.
Julian Gruendner - One of the best experts on this subject based on the ideXlab platform.
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ketos clinical decision support and machine learning as a service a training and deployment platform based on docker omop cdm and fhir web services
2019Co-Authors: Julian Gruendner, Thorsten Schwachhofer, Phillip Sippl, Nicolas Wolf, Marcel Erpenbeck, Christian Gulden, Lorenz A Kapsner, Sebastian Mate, Jakob Zierk, Michael StürzlAbstract:Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end User Application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end User Application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).
Wang Yumin - One of the best experts on this subject based on the ideXlab platform.
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implementation of vrml s network communication function based on java
2007Co-Authors: Wang YuminAbstract:VRML is a kind of virtual reality modeling language used on Internet,but it does not support network communication.The function of Script node is extended to support network communication which makes VRML nodes receive data through network and render the scene in real time.VRML and Java are independent of operating system platform,so are the Application systems built with the proposed method.Without considering the speed,the method can be used to rebuild the whole Internet network into a large-scale multi-User Application system.An Application example is implemented to illuminate the feasibility of the scheme.
Jakob Zierk - One of the best experts on this subject based on the ideXlab platform.
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ketos clinical decision support and machine learning as a service a training and deployment platform based on docker omop cdm and fhir web services
2019Co-Authors: Julian Gruendner, Thorsten Schwachhofer, Phillip Sippl, Nicolas Wolf, Marcel Erpenbeck, Christian Gulden, Lorenz A Kapsner, Sebastian Mate, Jakob Zierk, Michael StürzlAbstract:Background and objective To take full advantage of decision support, machine learning, and patient-level prediction models, it is important that models are not only created, but also deployed in a clinical setting. The KETOS platform demonstrated in this work implements a tool for researchers allowing them to perform statistical analyses and deploy resulting models in a secure environment. Methods The proposed system uses Docker virtualization to provide researchers with reproducible data analysis and development environments, accessible via Jupyter Notebook, to perform statistical analysis and develop, train and deploy models based on standardized input data. The platform is built in a modular fashion and interfaces with web services using the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard to access patient data. In our prototypical implementation we use an OMOP common data model (OMOP-CDM) database. The architecture supports the entire research lifecycle from creating a data analysis environment, retrieving data, and training to final deployment in a hospital setting. Results We evaluated the platform by establishing and deploying an analysis and end User Application for hemoglobin reference intervals within the University Hospital Erlangen. To demonstrate the potential of the system to deploy arbitrary models, we loaded a colorectal cancer dataset into an OMOP database and built machine learning models to predict patient outcomes and made them available via a web service. We demonstrated both the integration with FHIR as well as an example end User Application. Finally, we integrated the platform with the open source DataSHIELD architecture to allow for distributed privacy preserving data analysis and training across networks of hospitals. Conclusion The KETOS platform takes a novel approach to data analysis, training and deploying decision support models in a hospital or healthcare setting. It does so in a secure and privacy-preserving manner, combining the flexibility of Docker virtualization with the advantages of standardized vocabularies, a widely applied database schema (OMOP-CDM), and a standardized way to exchange medical data (FHIR).
