Biomedical data acquisition and processing in the Decision Support services of HEARTFAID platform

HEARTFAID project is to devise, develop and validate an advanced and innovative technological platform of services and end-user applications aiming at contributing towards the optimization of the clinical management of HF and the reduction of the economic and social costs, by collecting, integrating and processing all types of the above mentioned biomedical data and information. In particular, the early detection of HF related signs and symptoms and the appropriate identification and acquisition of biomedical data from myocardial tissue and organ, may contribute to delay the hospitalization and to improve both the quality of life and survival in pathologic patients. Chronic heart failure (CHF) is a complex cardiovascular syndrome whose management requires a complex clinical program involving the acquisition, integration and interpretation of heterogeneous biomedical data and information. Within the EU FP6 project HEARTFAID, a set of services has been developed to assist CHF stakeholders in their routine workflow and to provide an optimal management of CHF patients, by exploiting the most advanced technologies and instruments for diagnostic data processing. Innovative results on computational modelling, knowledge discovery methodologies, visualization and imaging techniques, and the medical knowledge of the relevant domain is appropriately integrated to design and develop an effective and reliable clinical decision support system: the HEARTFAID CDSS, corresponding to the core of HEARTFAID intelligence. This system is able to process clinical knowledge and patient-related information, intelligently filtered, processed and presented at appropriate times, to enhance patient care. It is devised as a service of the HEARTFAID platform for providing an effective support to the daily practice of the clinicians, by implementing adequate data processing algorithms, by providing guidelines to medical protocols as well as access to the knowledge base, by sending alarms in case of critical situations, and by supplying diagnostic suggestions. Two peculiar issues are involved in the development of the HEARTFAID CDSS for supporting medical decision making, i.e. (1) innovative approaches for biomedical signal and image processing; (2) robust and reliable reasoning approaches, based on Machine Learning and inference methodologies on declarative and procedural domain knowledge.