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COntinuous Multi-parametric and Multi-layered analysis Of DIabetes TYpe 1 & 2 (Commodity12)
Date du début: 1 oct. 2011, Date de fin: 30 juin 2015 PROJET  TERMINÉ 

In COMMODITY12 we will build a multi-layered multi-parametric infrastructure for continuous monitoring of diabetes type 1 and 2. The COMMODITY12 system will exploit multi-parametric data to provide healthcare workers and patients, with clinical indicators for the treatment of diabetes type 1 and 2. COMMODITY12 will focus on the interaction between diabetes and cardiovascular diseases. To address the 5.1b) Challenge under the FP7 ICT 7th, we propose a four-layered platform structured as follows:-Body Area Network Layer (BAN): this layer will employ sensors from the BodyTel PHS and additional Bluetooth sensors to monitor the patient physiological signals. This layer will perform multi-parametric aggregation of data for the Smart Hub layer.-The Smart Hub Layer (SHL): the BodyTel PHS at this layer receives aggregated data from the BAN and applies machine learning to classify the signals and provide indications about abnormalities in the curves. SHL will communicate with DRR over the cell-phone network.-The Data Representation And Retrieval Layer (DRR): this layer, based on the Portavita PHS to manage EHR, interfaces to the SHL and utilises existing medical data to perform information retrieval and produce structured information for the agents at the AIL.-The Artificial Intelligence Layer (AIL): this layer uses the DRR layer to retrieve structured background knowledge of the patient for intelligent agents applying diagnostic reasoning to the patient's condition.The system will be validated with diabetes (type 1 and 2) with a pilot in the form of a trial. The project outcome will aim to curb diabetes hospitalisation costs and to curb the percentage of diabetic patients experiencing cardiovascular complications. The main focus of our platform in Challenge 5.1 b) will be on "correlating the multi-parametric data with established biomedical knowledge to derive clinically relevant indicators".



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