MicroArt

Barcelona, Spain
Barcelona, Spain

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Estanyol F.,MicroArt | Rafael X.,MicroArt | Roset R.,MicroArt | Lurgi M.,MicroArt | And 2 more authors.
Knowledge Engineering Review | Year: 2011

Currently, biological databases (DBs) are a common tool to complement the research of a wide range of biomedical disciplines, but there are only a few specialized medical DBs for human brain tumour magnetic resonance spectroscopy (MRS) data; they typically store a limited range of biological data (i.e. clinical information, magnetic resonance imaging and MRS data) and are not offered as open-source Structured Query Language relational DB schemas. We present a novel approach to biological DBs: a distributed Web-accessible DB for storing and managing clinical and biomedical data related to brain tumours from different clinical centres. This tool is designed for multi-platform systems with dissimilar DB management systems. Being the main data repository of the HealthAgents (HA) project, it uses multi-agent technology and allows the centres to share data and obtain diagnosis classifications from other centres distributed around the world in a reliable way. The HA project aims to create an agent-based distributed decision support system (DSS) to assist doctors to provide a brain tumour diagnosis and prognosis. The HA DB enables the DSS to totally integrate with its Graphical User Interface to perform classifications with the stored data and visualize the results using the HA distributed agents framework. This new feature converts the system presented in the first application in the world to combine a storage and management tool for brain tumour data and a complete Web-based DSS to obtain automatic diagnosis. © Copyright Cambridge University Press 2011.


Lluch-Ariet M.,MicroArt | Lluch-Ariet M.,Polytechnic University of Catalonia | Pegueroles-Valles J.,Polytechnic University of Catalonia
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering | Year: 2011

As more and more data become available, the task of accessing and exploiting the large number of distributed clinical data repositories becomes increasingly complex. Moreover, accessing to a certain data set in a federated data warehouse may have constrains, and multilateral agreements may solve it. Such agreements may be very complex to be solved manually. Current systems for clinical data sharing do not support multilateral agreements. MOSAIC, intends to give a modular and efficient solution to the clinical data exchange problem with multilateral agreements. The proposed system takes advantage of agent based systems and the current standarised Interaction Protocols together with the current protocols for clinical data transfer. © 2011 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.


Xiao L.,University of Southampton | Xiao L.,Royal College of Surgeons in Ireland | Dasmahapatra S.,University of Southampton | Lewis P.,University of Southampton | And 10 more authors.
Knowledge Engineering Review | Year: 2011

In this paper, we analyze the special security requirements for software support in health care and the HealthAgents system in particular. Our security solution consists of a link-anonymized data scheme, a secure data transportation service, a secure data sharing and collection service, and a more advanced access control mechanism. The novel security service architecture, as part of the integrated system architecture, provides a secure health-care infrastructure for HealthAgents and can be easily adapted for other health-care applications. © Copyright Cambridge University Press 2011.


Hu B.,University of Southampton | Hu B.,SAP | Croitoru M.,LIRMM | Roset R.,MicroArt | And 6 more authors.
Knowledge Engineering Review | Year: 2011

In this paper we present our experience of representing the knowledge behind HealthAgents (HA), a distributed decision support system for brain tumour diagnosis. Our initial motivation came from the distributed nature of the information involved in the system and has been enriched by clinicians' requirements and data access restrictions. We present in detail the steps we have taken towards building our ontology starting from knowledge acquisition to data access and reasoning. We motivate our representational choices and show our results using domain examples used by clinical partners in HA. © Copyright Cambridge University Press 2011.

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