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Details of Grant 

EPSRC Reference: EP/T017961/1
Title: Cambridge Mathematics of Information in Healthcare (CMIH)
Principal Investigator: Schoenlieb, Professor C
Other Investigators:
Rudd, Dr J Fokas, Professor A Stewart, Mr GD
Samworth, Professor RJ Kourtzi, Professor Z Graves, Dr MJ
Gilbert, Professor FJ Jena, Dr R Aston, Professor JAD
Jiang, Dr H Sala, Dr E Erhun Oguz, Professor F
Lio, Dr P Williams, Dr GB O'Brien, Professor JT
Jefferson, Professor E Bohndiek, Dr SE van der Schaar, Professor M
Researcher Co-Investigators:
Project Partners:
AstraZeneca UK Limited Aviva Plc Cambridgeshire & Peterborough NHS FTrust
Canon Medical Research Europe Ltd Dassault Group Feedback Medical
GE Healthcare GlaxoSmithKline plc (GSK) National Physical Laboratory
Siemens Healthcare Ltd The Alan Turing Institute
Department: Applied Maths and Theoretical Physics
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 01 June 2020 Ends: 31 May 2023 Value (£): 1,295,778
EPSRC Research Topic Classifications:
Mathematical Analysis Mathematical Aspects of OR
Medical Imaging Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Healthcare
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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Summary
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Organisation Website: http://www.cam.ac.uk