Machine Learning for Patient-Specific, Predictive Healthcare Technologies via Intelligent Electronic Health Records

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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.