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The EPSRC Cambridge Mathematics of Information in Healthcare Hub (CMIH) is an interdisciplinary collaboration between mathematics, statistics, computer science, and medicine, and aims to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion. The overarching objective of the hub is to develop data analytics algorithms that are directly linked to the requirements in the clinic for healthcare decision making, focussing on some of the most challenging public health problems of our time, including: Cancer, Cardiovascular Disease and Dementia.

The foundation of the CMIH is built upon research and collaborations that started as part of the Centre for Mathematical Imaging in Healthcare, an EPSRC funded research centre launched in 2016. The Centre began with two aims. Firstly to bring together researchers in applied mathematics and statistics with a focus on clinical imaging, and secondly to translate state of the art research in both fields into clinical applications. This work 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. With applications for personalised diagnosis and treatment, as well as target identification and validation on a population level this work is of the utmost importance.

More information on the previous work conducted as part of the Centre for Mathematical Imaging in Healthcare is available here.

 

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The Cambridge Mathematics of Information in Healthcare Hub (CMIH) is a collaboration between mathematics, statistics, computer science and medicine, aiming to develop robust and clinically practical data analytics algorithms for healthcare decision making. Our work focusses on some of the most challenging public health problems; Cancer, Cardiovascular Disease, and Dementia.

 

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