Personalised Disease Modelling using Topological Data Analysis and Pseudo Time
The National Health Service states that the ‘one size fits all’ approach to the care and treatment of patients is ineffective as everyone responds to diseases differently. The need for personalised medicine arises so that we can better manage patients’ health based on an individual patient’s medical background including their genomic data. This project aims to use state-of-the-art Artificial Intelligence techniques in order to learn the “shape” of disease as it progresses. This will enable models to be built that capture realistic progression for an individual patient, facilitating better management of disease and more appropriate interventions.
The figure below shows how a network can represent stages in diabetes and an overall shape with starting points and endpoints (inferred from real patient data).
Meet the Principal Investigator(s) for the project
Dr Allan Tucker - Allan Tucker is Reader in the Department of Computer Science where he heads the Intelligent Data Analysis Group consisting of 17 academic staff, 15 PhD students and 4 post-docs. He has been researching Artificial Intelligence and Data Analytics for 21 years and has published 120 peer-reviewed journal and conference papers on data modelling and analysis. His research work includes long-term projects with Moorfields Eye Hospital where he has been developing pseudo-time models of eye disease (EPSRC - £320k) and with DEFRA on modelling fish population dynamics using state space and Bayesian techniques (NERC - £80k). Currently, he has projects with Google, the University of Pavia Italy, the Royal Free Hospital, UCL, Zoological Society of London and the Royal Botanical Gardens at Kew. He is academic lead on an Innovate UK, Regulators’ Pioneer Fund (£740k) with the Medical and Health Regulatory Authority on benchmarking AI apps for the NHS. He serves regularly on the PC of the top AI conferences (including IJCAI, AAAI, and ECML) and is on the editorial board for the Journal of Biomedical Informatics and Medical Informatics and Decision Making. He is hosting a special track on "Explainable AI" at the IEEE conference on Computer Based Medical Systems in 2019. He has been widely consulted on the ethical and practical implications of AI in health and medical research by the NHS, and the use of machine learning for modelling fisheries data by numerous government thinktanks and academia.
Related Research Group(s)
Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.
Project last modified 21/06/2021