Cambridge Mathematics of Information in Healthcare (CMIH)

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.

The interdisciplinary approach relating fundamental mathematical research to the life sciences is desired but rarely delivered. Our experience in the current Cambridge EPSRC maths in healthcare centre has taught us that the main reason for this is the considerable effort needed to deeply engage with researchers in other disciplines outside of one's own. Genuinely impactful applied mathematical work needs strong relationships and trust between investigators. The CMIH already has a strong track-record of delivery with > 100 papers (approximately 50/50 in mathematical and medical journals and > 1000 citations), >10 medical image analysis software packages, 30 active interdisciplinary and industrial collaborations, and >£18M of further research funding. We thus believe that our new Hub will start from a position of great strength to develop clinically-purposeful algorithms (in close collaboration with clinicians) and to turn those into clinically practical AI tools (the latter will be supported by the Dundee HDR UK arm, i.e. Emily Jefferson).

Beyond academic beneficiaries, the hub will focus on using this advantageous position to deliver impact in healthcare areas. The location of the hub lends itself to benefit from the numerous related activities already happening within Cambridge, where interdisciplinary biomedical research is second to none. Engaging with partners like Cancer Research UK Cambridge Institute, the Wellcome Trust Genome Campus or the NIHR Cambridge Biomedical Research Centre significantly increases the likelihood of tangible impact being delivered at the hub. Furthermore, we will work with the other EPSRC funded hubs to maximise the joint impact provided to healthcare users and the general public more widely.

Developments in healthcare technologies have far reaching impacts beyond the academic research alone. Improvements in clinical decision making processes through the integration of multi-modal data and AI will advance patient treatments and care, thus improving outcomes while reducing costs. Beneficiaries include the clinicians themselves and the healthcare organisations they represent, the NHS (and other equivalent health services internationally), patients and ultimately the general public. Furthermore, the IP generated from the hub and the subsequent technological innovation will benefit technology companies and the wider healthcare tech community, further improving the ability of healthcare providers to diagnose, treat and care for patients and their families.