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Details of Grant 

EPSRC Reference: EP/P009964/1
Title: PAMBAYESIAN: PAtient Managed decision-support using Bayesian networks
Principal Investigator: Fenton, Professor N
Other Investigators:
Patel, Professor A Tzortziou Brown, Dr V Huda, Dr MS
Curzon, Professor P Neil, Professor M Morrissey, Dr D
Alomainy, Dr A Hitman, Professor GA Marsh, Dr W
Collier, Dr DJ Humby, Dr F
Researcher Co-Investigators:
Project Partners:
Be More Digital Ltd Hasiba Medical GmbH IBM
Mediwise Ltd Rescon Technologies SMART Medical Limited
uMotif Limited
Department: Sch of Electronic Eng & Computer Science
Organisation: Queen Mary University of London
Scheme: Standard Research
Starts: 01 July 2017 Ends: 30 June 2020 Value (£): 1,538,497
EPSRC Research Topic Classifications:
Artificial Intelligence Human-Computer Interactions
Information & Knowledge Mgmt Mobile Computing
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Healthcare
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Sep 2016 Intelligent Technologies Prioritisation Panel Announced
Summary on Grant Application Form
Patients with chronic diseases must take day-to-day decisions about their care and rely on advice from medical staff to do this. However, regular appointments with doctors or nurses are expensive, inconvenient and not necessarily scheduled when really needed. Increasingly, there are low cost and highly portable sensors that can measure a wide range of physiological values. Can such 'wearable' sensors be used to improve the way that chronic conditions are managed? Patients could have more control over their own care if they wished; doctors and nurses could monitor their patients without the expense and inconvenience of visits, except when they are actually needed. Remote monitoring of patients is already in use for some conditions but there are barriers to its wider use: it relies too much on clinical staff to interpret the sensor readings; patients, confused by the information presented, may become more dependent on health professionals, whose work may be increased rather than reduced.

The project seeks to overcome these barriers by addressing two weaknesses of the current systems. First is their lack of intelligence. Intelligent systems that can help medical staff in making decisions already exist and can be used for diagnosis, prognosis and advice on treatments. One especially important form of these systems uses belief or Bayesian networks, which show how the relevant factors are related and allow beliefs, such as the presence of a medical condition, to be updated from the available evidence. However, these intelligent systems do not yet work easily with data coming from sensors. The second weakness is any mismatch between the design of the technical system and the way the people - patients and professional - interact. We will work on these two weaknesses together: patients and medical staff will be involved from the start, enabling us to understand what information is needed by each player and how to use the intelligent reasoning to provide it. The medical work will be centred on three case studies, looking at the management of rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation (irregular heartbeat). These have been chosen both because they are important chronic diseases and because they are investigated by significant research groups in our Medical School, who are partners in the project. This makes them ideal test beds for the technical developments needed to realise our vision and allow patients more autonomy in practice.

To advance the technology, we will design ways to create belief networks for the different intelligent reasoning tasks, derived from an overall model of medical knowledge relevant to the diseases being managed. Then we will investigate how to run the necessary algorithms on the small computers attached to the sensors that gather the data as well as on the systems used by the healthcare team. Finally, we will use the case studies to learn how the technical systems can integrate smoothly into the interactions between patients and health professionals, ensuring that information presented to patients is understandable, useful and reduces demands on the care system while at the same time providing the clinical team with the information they need to ensure that patients are safe.

If successful, our results will be useful not only for the examples of chronic diseases studied on the project but also for managing other chronic medical conditions, when the same techniques can be applied. Although the project will produce prototype systems, several stages of product development and clinical trials will be needed before real systems are available for patients; we will prepare for these and make a first evaluation of the economic benefits of the proposed systems during the project. Also, several technology companies are involved in the project's Advisory Board to help ensure effective commercial exploitation in the long run.

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