| EPSRC Reference: |
EP/T017961/1 |
| Title: |
Cambridge Mathematics of Information in Healthcare (CMIH) |
| Principal Investigator: |
Schönlieb, Professor C |
| Other Investigators: |
| Jena, Dr R |
Stewart, Professor GD |
Aston, Professor Sir JAD |
| Sala, Dr E |
Erhun Oguz, Professor F |
Lio, Dr P |
| Bohndiek, Dr SE |
Fokas, Professor A |
O'Brien, Professor JT |
| Rudd, Professor J |
Jefferson, Professor E |
van der Schaar, Professor M |
| Jiang, Professor H |
Samworth, Professor RJ |
Gilbert, Professor FJ |
| Kourtzi, Professor Z |
Williams, Dr GB |
Graves, Professor MJ |
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| Project Partners: |
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| 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
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| EPSRC Research Topic Classifications: |
| Mathematical Analysis |
Mathematical Aspects of OR |
| Medical Imaging |
Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
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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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cam.ac.uk |