UPDATE 9 Feb 2017: Various Research Fellowship and PhD vacancies funded by this project are now advertised. See
here.
Queen
Mary has been awarded a grant of £1,538,497 (Full economic cost
£1,923,122) from the EPSRC towards a major new collaborative project to
develop a new generation of intelligent medical decision support
systems. The project, called PAMBAYESIAN (Patient Managed
Decision-Support using Bayesian Networks) focuses on home-based and
wearable real-time monitoring systems for chronic conditions including
rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation. It
has the potential to improve the well-being of millions of people.
The project team includes researchers from both the
School of Electronic Engineering and Computer Science (EECS) and
clinical academics from the Barts and the London School of Medicine and
Dentistry (SMD). The collaboration is underpinned by extensive research
in EECS and SMD, with access to digital health firms that have extensive
experience developing patient engagement tools for clinical development
(BeMoreDigital, Mediwise, Rescon, SMART Medical, uMotif, IBM UK and
Hasiba Medical).
The project is led by
Prof Norman Fenton with
co-investigators: Dr William Marsh, Prof Paul Curzon, Prof Martin Neil,
Dr Akram Alomainy (all EECS) and Dr Dylan Morrissey, Dr David Collier,
Professor Graham Hitman, Professor Anita Patel, Dr Frances Humby, Dr
Mohammed Huda, Dr Victoria Tzortziou Brown (all SMD). The project will
also include four QMUL-funded PhD students.
The three-year project will begin June 2017.
Background
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 needed.
Increasingly, we are seeing the use of low cost and highly portable
sensors that can measure a wide range of physiological values. Such
'wearable' sensors could improve the way 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 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; remote sensor use may
then lead to an increase in medical assistance, rather than reduction.
The project seeks to overcome these barriers by addressing two key weaknesses of the current systems:
- 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.
- 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.
Further information: www.eecs.qmul.ac.uk/~norman/projects/PAMBAYESIAN/
This project also complements another Bayesian networks based project - the Leverhulme-funded project "
CAUSAL-DYNAMICS (Improved Understanding of Causal Models in Dynamic Decision Making)" - starting January 2017. See
CAUSAL-DYNAMICS
