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Details of Grant 

EPSRC Reference: EP/T017961/1
Title: Cambridge Mathematics of Information in Healthcare (CMIH)
Principal Investigator: Schönlieb, Professor C
Other Investigators:
Rudd, Professor J Jefferson, Professor E Jiang, Professor H
Samworth, Professor RJ Kourtzi, Professor Z Williams, Dr GB
Graves, Professor MJ van der Schaar, Professor M Erhun Oguz, Professor F
Jena, Dr R Stewart, Professor GD Aston, Professor Sir JAD
Sala, Dr E Lio, Dr P Bohndiek, Dr SE
Fokas, Professor A O'Brien, Professor JT Gilbert, Professor FJ
Researcher Co-Investigators:
Project Partners:
AstraZeneca Aviva Plc Cambridgeshire & Peterborough NHS FT
Canon Medical Research Europe Ltd Dassault Systemes Feedback Medical
GE Healthcare GlaxoSmithKline plc (GSK) National Physical Laboratory NPL
Siemens Healthineers The Alan Turing Institute
Department: Applied Maths and Theoretical Physics
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 01 September 2020 Ends: 31 December 2023 Value (£): 1,295,778
EPSRC Research Topic Classifications:
Mathematical Analysis Mathematical Aspects of OR
Medical Imaging Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
02 Dec 2019 Hubs for Mathematical Sciences in Healthcare Deferred
11 Feb 2020 Hubs for Mathematical Sciences in Healthcare Interviews Announced
Summary on Grant Application Form
In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future.

Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK.

Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.cam.ac.uk