Wednesday, 8 February 2017

Helping US Intelligence Analysts using Bayesian networks

Causal Bayesian networks are at the heart of a major new collaborative research project led by Australian University Monash  - funded by the United States' Intelligence Advanced Research Projects Activity (IARPA). The objective is to help intelligence analysts assess the value of their information. IARPA was set up following the failure of the US intelligence agencies to properly assess the correct levels of threat posed by Al Qaeda in 2001 and Iraq in 2003.

The chief investigator at Monash, Kevin Korb, said in an interview in the Australian:
"..quantitative rather than qualitative methods were crucial in judging the value of intelligence.... more quantitative approaches could have helped contain the ebola epidemic by making authorities appreciate the scale of the problem months earlier. They could also build a better assessment of the likelihood of events like gunfire between vessels in the South China Sea, a substantial devaluation of the Venezuelan currency or a new presidential aspirant in Egypt."
Norman Fenton and Martin Neil (both of Agena and Queen Mary University of London) will be working on the project along with colleagues such as David Lagnado and Ulrike Hahn at UCL.  AgenaRisk will be used throughout the project as the Bayesian network platform.

Further information:

Queen Mary in new £2 million project using Bayesian networks to create intelligent medical decision support systems with real-time monitoring for chronic conditions

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.


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:
  1. 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.
  2. 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:

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