Domenico Conforti, Universitŗ della Calabria, Italy
Manolis Tsiknakis, Foundation for Research and Technology - Hellas, Institute of Computer Science, Greece
Joerg Habetha, Philips Technologie GmbH, Research Group "Medical Signal Processing", Germany
JoŽl Bacquet, European Commission, Information Society & Media DG, Belgium
Decision Support Systems, Medical Ontologies, Medical Knowledge Discovery, Medical
Knowledge Representation, Medical Knowledge Management.
Health care government policy makers, health insurance companies, service providers and user’s organizations are changing the face of health care delivery, especially across the developed western countries, requesting that the quality and the cost efficiency of the healthcare, the safety and the empowerment of the patients play an ever more crucial role in the management of the national and regional healthcare systems. In particular, the following issues are increasing the pressure on the healthcare systems:
- consumers are more and more demanding for high quality care;
- managers are struggling to deliver health care services at reasonable costs while, on the other hand, there is an increasing diffusion of chronic diseases as well as, due to the progressive ageing of the population, a rising number of patients that need long-term continuous health assistance;
- clinicians are placing increasing emphasis on the practice of evidence-based medicine whereas, at the same time, citizens are improving their sensitivity regarding the clinical risk management.
- health care systems are seeking to meet these demands by operating into a framework of continuous quality improvement.
Improving health care quality while reducing costs requires the elimination of unintended and unnecessary overhead in the entire care process (prevention – diagnosis – prognosis – therapy) and the application of new and more accurate procedures for the clinical risk management. To this end, e-Health technologies and applications can play an ever greater and crucial role.
In fact, during the last years we have been assisting to an increasing development of high methodological and technological effective solutions (such as tools for Medical Knowledge Discovery
and Clinical Decision Support System
prototypes) to foster evidence based medicine and best clinical practices. These solutions have the potentialities to help in reducing unreliability and errors by improving effectiveness and efficiency.
Under this respect, the main aim of the proposed Special Session is to present, analyze and discuss new research trends about the devising and development of advanced approaches for medical ontologies and medical knowledge discovery, representation and management and how these issues can impact on the architectural organization and development of effective and efficient Clinical Decision Support Systems.
These topics will be deeply discussed and assessed through the current activities of three EU FP6
projects, strongly involved in the development and application of the mentioned technologies and
methodologies within several clinical domains, namely HEARTFAID, ACGT and MyHEART.
HEARTFAID is a research and development project aimed at devising, developing and validating
an innovative knowledge based platform of services, able to improve early diagnosis and to make
more effective the medical-clinical management of heart diseases within elderly population.
ACGT aims to present the 'next-step' in cancer research and fill-in the technological gaps of
clinical trials targeting two forms of cancer: breast cancer and paediatric nephroblastoma. ACGT
will develop a Biomedical GRID infrastructure supporting seamless mediation services for sharing
clinical and genomic expertise. It will help to identify quicker and more efficiently the
characteristics that determine what form of treatment best suits which patient.
MyHEART is an integrated project aiming to develop intelligent systems for the prevention and
monitoring of cardiovascular diseases. The project develops smart electronic and textile systems
and appropriate services that empower the users to take control of their own health status.
All the above projects are working on the design and development of new and more efficient
approaches for heterogeneous biomedical data acquisition and integration, for the development of
effective medical knowledge bases and the implementation of inference engines in order to realize
reliable and useful Clinical Decision Support Systems within the Cardiovascular and Oncology