Scott's initial investigations into Learning Health Systems (LHS) started out of a comment by William Marsh (QMUL) while showing Scott around the QMUL Mile End campus on his first day. Moving forward to 2018, Scott had amassed a significant collection of data on LHS and was looking for ways to present the knowledge from that data in a way that would benefit all who are investigating or seeking to implement LHS. A chance meeting with Henry Potts (UCL) and Owen Johnson (Leeds) at a Health Informatics conference saw the beginnings of a collaboration that presently spans five universities.
Through his current work with QMUL's RIM group and their ongoing EPSRC-funded Pambayesian project, Scott and the rest of our team bring a wealth of experience, knowledge and a growing list of publications on LHS, health IT, precision medicine, health law and ethics that will help AKINAD to be a leading centre of excellence for LHS.
Pambayesian is a 3-year EPSRC-funded project awarded to Dr Norman Fenton (PI)of Queen Mary University of London, to run from June 2017 to May 2020. Pambayesian aims 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 Bayes-Knowledge research team uses Bayesian Networks to assess risk and aid decision-making in a wide range of applications. Using information smartly is the fastest path to the best decision: "dynamic discretization" is a tool for BNs created by the Bayes-Knowledge project, which allows the necessary discretization of continuous variables to be optimised through an iterative process. This allows us to model every variable in the BN with minimum inefficiency and maximum accuracy, and probability distributions for continuous variables need not be constrained to a convenient shape.
CAUSAL-DYNAMICS (“Improved Understanding of Causal Models in Dynamic Decision-making”) is a 3-year project (starting Jan 2017) at Queen Mary funded by a Leverhulme Trust Research Project Grant of £385,510. The project ultimately will lead to improved design and use of self-monitoring systems such as blood sugar monitors, home energy smart meters, and self-improvement mobile phone apps. It is a collaborative project, led by Professor Norman Fenton of the School of Electronic Engineering and Computer Science, with co-investigators Dr Magda Osman (School of Biological and Chemical Sciences), Prof Martin Neil (School of Electronic Engineering and Computer Science) and Prof David Lagnado (Department of Experimental Psychology, University College London).