Self Care system for Heart Failure out of hospital patients

CUORE is a Heart Failure (HF) Disease Assessment System that makes use of innovative approaches, based on Information Technologies (IT) and portable monitoring devices, for the continuous assessment of HF progression and cardiovascular risk stratification. The system valuates the cardiac condition integrating patient data from different sources with special emphasis in the information obtained for ECG processing. Rather than just evaluate the cardiovascular status, the system also aims to motivate patients to have an active role in their health management and to improve their cardiac condition by promotion of an active lifestyle. In order to make the system usable the methodology adopted to create the final solution is iterative and it involves users in all stages. This paper presents the conceptualization of CUORE as a solution to self care for heart failure out of hospital patients

A data gathering framework to collect Type 2 diabetes patients data

In this work, we present a framework implemented within the EU project MOSAIC, funded under the FP7 framework, to gather Type 2 Diabetes (T2D) patients' data coming from three European hospitals and a local health care agency. A subset of the MOSAIC activities is centered on the development of Temporal Data Mining models to identify relevant clinical pathways in patients' histories and will in particular benefit from the data coming from the medical centers involved in the project. To best exploit this repository, the need for creating a common and sharable data model becomes immediately apparent. This model is the main subject of this paper. The proposed approach relies on the Informatics for Integrating Biology and the Bedside (i2b2) and the Shared Health Research Information Network (SHRINE) open source software tools. It provides an integrated research setting to merge clinical and environmental data that will enable obtaining a broader vision of individual patients' histories, which will be then mined with multivariate models to identify relevant clinical pathways