Health-e-Child - An integrated platform for European paediatrics based on a Grid-enabled network of leading clinical centres
Project website:  Health-e-Child 
Principal Investigator: Yannis Ioannidis
Duration: 01/01/2006 - 30/04/2010

There is a compelling demand for the integration and exploitation of heterogeneous biomedical information for improved clinical practice, medical research, and personalised healthcare for the citizens of the EU.

The Health-e-Child project aims at developing an integrated healthcare platform for European paediatrics, providing seamless integration of traditional and emerging sources of biomedical information. The long-term goal of the project is to provide uninhibited access to universal biomedical knowledge repositories for personalised and preventive healthcare, large-scale information-based biomedical research and training, and informed policy making.

The Health-e-Child project focus will be on individualised disease prevention, screening, early diagnosis, therapy and follow-up of paediatric heart diseases, inflammatory diseases, and brain tumours.

The project will build a Grid-enabled European network of leading clinical centres that will share and annotate biomedical data, validate systems clinically, and diffuse clinical excellence across Europe by setting up new technologies, clinical workflows, and standard.

In the context of HeC our group is involved in two areas: Knowledge Discovery & Medical Query processing.

Our work includes AITION, a State of the Art knowledge discovery tool based on Bayesian networks, supported by our Medical Processing Engine. AITION is used in a variety of knowledge discovery problems, in three different medical domains in the context of the project. ACL (Aition CLeanup system), to help us preprocess, validate, correct, and discretized raw data out of the project data collection databases.

MPE (Medical Processing Engine), a distributed query processing system. MPE provides a data flow processing engine, a high level query language, optimized to run on a distributed multiprocessing platform, such us, the grid, ad-hoc clusters, and clouds. Finally, The MPE language is extensible since user defined operators may be added to the system and used by queries.

In the context of HeC our group is involved in two areas: Knowledge Discovery & Medical Query processing.

Our work includes AITION, a State of the Art knowledge discovery tool based on Bayesian networks, supported by our Medical Processing Engine. AITION is used in a variety of knowledge discovery problems, in three different medical domains in the context of the project. ACL (Aition CLeanup system), to help us preprocess, validate, correct, and discretized raw data out of the project data collection databases.

MPE (Medical Processing Engine), a distributed query processing system. MPE provides a data flow processing engine, a high level query language, optimized to run on a distributed multiprocessing platform, such us, the grid, ad-hoc clusters, and clouds. Finally, The MPE language is extensible since user defined operators may be added to the system and used by queries.

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