@neurIST: Infrastructure for Advanced Disease Management Through Integration of Heterogeneous Data, Computing, and Complex Processing Services

Abstract

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

The VPH-Share Data Management Platform: Enabling Collaborative Data Management for the Virtual Physiological Human Community

“…Offering two well-established types of service interface technologies will simplify the integration of dataset services into client toolkits that are based on different programming languages. Dataset and virtual dataset services have been developed on the basis of the Vienna Cloud Environment (VCE) middleware [12], [13], [14], [15] and utilize technology developed in the context of OGSA-DAI [16] and are available via the Dataset Service Environment (DSE).…”

Archetype-based semantic mediation: Incremental provisioning of data services

“…While traditional information modeling methodologies produce large and detailed domain-specific models [3,8], the two-level modeling paradigm does not specify a predefined set of attributes that can be represented.…”

“…For 1 http://openehr.org/knowledge/ example, the EU @neurIST project 2 created a generic IT infrastructure for the management of cerebral aneurysms [3]. Data integration in @neurIST was generic in scope, and was based on the Vienna Grid Environment [4] to mediate different data sources via Web services utilizing OGSA-DAI and OGSA-DQP 3 . On top of these Web services the project incorporates a domain ontology [6] and rich annotations to setup data mediation services according to selected ontology concepts [14].…”

“…The population with data resulted in a large patient information database distributed across Europe and comprising data of over 1.300 subjects with aneurysms. This database was not materialized in a centralized database, but it was presented to the user as a single (virtual) data source through the mediation services used in the project [3].…”

“…Service-oriented architectures are usually employed to enable unified access to different information sources along with extensive descriptions of all data-sources, through often domain-specific ontologies. Many recent biomedical research projects [3,7,8,11,16] employed this approach, but also required huge efforts to adopt semantic technologies. On the one hand, ontological concepts need to be linked to actual data (annotation), and on the other hand, integration services must be defined based on semantic concepts (semantic data mediation).…”