PAMBAYESIAN: PAtient Managed decision-support using Bayesian networks
PAMBAYESIAN (
Patient Managed Decision-Support using Bayesian Networks) is a 3-year EPSRC funded project awarded to Queen Mary (June 2017-May 2020) to develop a new generation of intelligent medical decision support systems. The project focuses on home-based and wearable real-time monitoring systems for chronic conditions including rheumatoid arthritis, diabetes in pregnancy and atrial fibrillation. The project has the potential to improve the well-being of millions of people. EPSRC is contributing a grant of £1,538,497 towards the cost of the project, which is a collaboration between 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 project is also supported by 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.
Press Release October 2016: Queen Mary’s new £2 million project to create intelligent medical decision support systems with real-time monitoring for chronic conditions.
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).
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.
Summary
Medical decision support systems based on Bayesian Networks (BNs) – that combine expert judgment with data and incorporate causal knowledge – consistently outperform data-driven ‘score’ based systems commonly used for risk assessment. Such BN models have been developed for a wide range of medical applications and, in theory, could replicate much routine analysis currently undertaken by GPs examining patients in a surgery. It is increasingly well understood how the expert driven BN decision-support models can be built, and progress is being made with their introduction into routine clinical use. However, they remain primarily tools for medical staff, rather than for patients, used as part of existing care pathways with visits to a clinic, GP surgery or hospital. Moreover, the data used is ‘low frequency’ (such as when a doctor enters an observation) and decision-making is infrequent (often even just ‘one-time only’ such as whether or not a treatment should be applied), at intervals determined by medical staff.
In parallel with the BN developments, it has become feasible to continuously monitor patients with portable or wearable sensors, covering an increasing range of physiological parameters. In theory these monitoring devices, along with other home treatment devices, could be provided with the ‘intelligence’ of an expert-built BN model to support patients directly with more care at home and fewer visits to medical centres. However, there are a range of complexity challenges that must be overcome to build the necessary bridge between BN models and devices usable by patients in the home. Thus, the goal of this project is to overcome these complexity challenges to develop a framework for a new generation of intelligent medical decision support systems including their real-time monitoring – based on expert built BNs.
The research framework will be informed by, and validated with, two major medical case studies of chronic conditions where there is potential for self-monitoring and treatment in the home, namely: musclo-skeletal conditions and diabetes. A third case study on cardiac arrhythmia will be used to show that the new framework is widely applicable.
The project is a collaboration involving a diverse team of senior researchers in the Faculty of Science and Engineering (who will lead the different strands of the ICT research) and clinical academics from the Barts and the London School of Medicine and Dentistry School (who have proposed, and will supervise, the case studies, will support the pathways to impact and engagement with medical practitioners, and provide analysis of the potential benefits). QMUL’s belief in both the importance of the research and capability of the team is demonstrated by its commitment to fund four full time PhD students to work on the case studies if the proposal is successful.
To ensure optimal pathways to impact, we will use a participatory design approach to involve patient groups from the start, as we know that technology must be usable by non-specialists. Also, safety (from the perspective of the human interface of devices and their intrinsic functionality) is a core research objective, meaning that the traditional ‘regulatory barrier’ to actual deployment will be easier to penetrate. We have also planned Patient and Public Involvement workshops, focus groups, and consultation events with medical practitioners with special focus on GPs. The team is also internationally renowned for its strength in public engagement, and will produce publications pitched at medical practitioners, regulators, and the general public.
