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Machine Learning for Intelligent Healthcare Technologies

The EPSRC has funded eight new research projects that will help patients manage their health at home.The Oxford programme, named “ASPIRE”, and led by Professor David Clifton from the Department of Engineering Science, is funded with £1.9 million.The team includes the Computational Health Informatics (CHI) Lab, led by Professor Clifton, and the Department's Machine Learning Research Group, led by Professors Steve Roberts and Michael Osborne.

ASPIRE will develop an “intelligent” home-based system, with smart algorithms embedded within lightweight healthcare sensors. Its novel informatics will incorporate next-generation machine learning algorithms to combine information from wearable sensors with medical information from GP and hospital visits.

This will enable the system to learn the “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. Project partners include Microsoft Research and Oxford University Hospitals NHS Foundation Trust.

Professor David CliftonProfessor David Clifton said that: ‘ASPIRE is an exciting opportunity to build on Oxford’s integrated strengths in machine learning and healthcare for providing ‘joined-up’ care that bridges the boundary between hospital and home.  This is a priority for the UK National Health Service, and is an area in which principled engineering is a fundamental component.’

Professor Philip Nelson, Chief Executive of the Engineering and Physical Sciences Research Council (EPSRC), said: ‘The UK has an aging population and the demands on our health services are growing. Monitoring chronic conditions through outpatients’ clinics is both costly and time consuming for patients, surgeries and hospitals. Using these new technologies provides ways of gauging a patient’s health in real-time and detecting any deterioration quickly. This will help people remain in their homes for longer, avoid congestion and delay and mean treatment can be targeted quickly and when it can be most appropriate and effective.’

Published on 09 December 2016