There is an urgent, unmet need for reliable, intelligent systems that can monitor patient condition in the home, and which can help patients manage long-term conditions. Delays in recognition of the changes in physiological state worsen outcomes and increase healthcare costs. The ASPIRE programme uses chronic obstructive pulmonary disorder (COPD) as an exemplar, which affects over 210 million people globally. This condition costs the National Health Service over £800 million each year, over half of which is spent treating patients in hospital, rather than caring for them in their homes. Intelligent monitoring systems are required to address the needs of patients with long-term conditions in their homes. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for monitoring; (ii) a lack of robustness in the estimates of the vital signs that wearable sensors produce; (iii) very limited battery life that requires batteries to be re-charged at a rate that prevents their use on a large scale; and (iv) limited subsequent use of the data for helping the patient understand and manage their condition. We propose to develop an "intelligent" home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome these limitations. Our novel work will incorporate next-generation machine learning algorithms to combine information from healthcare sensors with information from GP and hospital visits. This will enable the system to learn "normal" health condition for individual patients, with knowledge of other conditions from which they may be suffering, and which can then make recommendations to the patient concerning self-management of their condition. This work will include close working with world-leading clinicians to ensure that the recommendations provided by the system are correct for the individual patient.

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.