Moreover, HealthAgents intends to build a completely distributed repository with local databases. Grid technologies such as multi-site data partition and distributed data sharing will permit the seamless access to different databases across sites.

We argue that a d-DSS will furnish a completely new approach to brain tumour diagnosis. Since inferences from local predictions may well conflict with one another, reasoned argument between intelligent agents, acting on behalf of scientists, in a multi-agent system, will foster consensus.

The HealthAgents project intends not only to apply agent technology to the biomedical field in a multi-disciplinary fashion, but also develop the first distributed repository for brain tumour diagnosis, leading eventually to the formation of a special interest data grid.

Status  

A centralised DSS is already available from the INTERPRET project to facilitate the clinical use of MRS in brain tumour diagnosis which uses a classification based on histopathological diagnosis. A more elaborated DSS, combining MRS biochemical profiles from Single Voxel (SV) and metabolic spatial distribution by Chemical Shift Imaging (CSI) MRS in vivo data, is currently being developed and implemented in the eTUMOUR project. The eTUMOUR DSS will eventually improve and facilitate the clinical application of MRS in adults and paediatric brain tumour diagnosis, prognosis and treatment selection using a classification based on the combination of histology results and high resolution metabolic profiles (HR-MAS) and transcriptomic (DNA micro-arrays) ex vivo data.

The HealthAgents d-DSS will build upon these projects and include additional MRS data, such as childhood tumours and less common adult tumours, using new classifications based on genetics. The development of this new d-DWH (the “d-DSS”), incorporating concepts of networking, agent technology, and data mining, must increase the number of accessible cases, yielding to an improved classifier, that in turn will achieve the goals described in this proposal.

HealthAgents proposes a multi-layer system architecture as depicted in the following figure. The database-mapping layer is used to map a relational database schema to the HealthAgents ontological schema. The programming API layer abstracts the underlying database interaction from the agent architecture. The business methods layer contributes to the main control flow of an agent such as the new case classification, new classifiers reception, and data retrieval.

The security and trust layer is a crucial system component due to the sensitivity of the data. Its functionalities are access control, data marshalling, tracking of on-going data, and the evaluation of reputation and trust of agents. The agent layer is in charge of all the communications and allows their abstraction from the rest of the system to allow flexibility in the underlying framework. The semantic description layer will contain the description of what the agent holds and what it is able to do.

Timeline