Cardiovascular diseases are responsible for the death of about 4 million individuals per year in Europe. The associated health care costs are estimated at 10 billion euros (8 billion pounds) for the UK alone. One of the most common vascular diseases is abdominal aortic aneurysm. It can be described as a localised, irreversible permanent dilatation of the aorta, the main artery in the human body. Generally no obvious symptoms are exhibited by patients, and if left untreated this abnormal dilation tends to grow until rupture. This event is life threatening, in the vast majority of cases, and it is responsible for 8000 deaths every year in the UK. Aneurysm, once diagnosed, is routinely treated by surgical repair but this operation is not easy and presents some risks. Hence, it is of extreme importance that an early diagnosis of the disease is developed with prompt action when requested. This present research proposal aims to improve our understanding regarding the mechanical causes leading to aneurysm formation and the possibility of enhancing our ability to predict aneurysm rupture. A series of experimental investigations of healthy and damaged aorta are planned to acquire data to characterize the mechanical behaviour of aneurysms. This is a necessary step to produce an advanced numerical model that can improve our understanding of the disease. A preliminary comparison of experimental observations with numerical predictions will help the verification of computational methodologies that will be applied to patient specific geometries available as medical images. This work will allow clinicians to make better informed decisions for endovascular repair based on a more detailed knowledge of aneurysm development beyond the currently accepted criterion of maximum diameter, thus benefiting affected patients and society as a whole and leading to significant cost savings by reducing morbidity and premature death. Furthermore, this research will help the UK to maintain its worldwide recognized high standard in healthcare.

Healthcare systems world-wide are struggling to cope with the demands of ever-increasing populations in the 21st-century, where the effects of increased life expectancy and the demands of modern lifestyles have created an unsustainable social and financial burden. However, healthcare is also entering a new, exciting phase that promises the change required to meet these challenges: ever-increasing quantities of complex data concerning all aspects of healthcare are being stored, throughout the life of a patient. These include electronic health records (EHRs) now active in many hospitals, and large volumes of data being collected by patient-worn sensors. The resulting rapid growth in the amount of data that is stored far outpaces the capability of clinical experts to cope. There is huge potential for using advances in computer science to use these huge datasets. This promises to improve healthcare outcomes significantly by allowing the development of new technologies for healthcare using the data - this is an area that promises to develop into a major new field in medicine. Making sense of the complex data is one of the key challenges for exploiting these massive datasets. This programme aims to establish a new centre focussed on developing the next generation of predictive healthcare technologies, exploiting the EHR using new methods in computer science. We describe a number of healthcare themes which demonstrate the potential to improve patient outcomes. This will be achieved in collaboration with a consortium of leading clinicians and healthcare companies. The primary aim is to develop the "Intelligent EHR", which will have applications in creating "early warning systems" to predict patient problems (such as heart failure), and to help doctors know which drug or treatment would best be used for each individual patient - by interpreting the vast quantities of data available in the EHR.