Title: Cambridge Mathematics of Information in Healthcare (CMIH)
Funder: Engineering and Physical Sciences Research Council

Abstract: 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.

Lay abstract: The overarching objective of the hub is to develop data analytics algorithms and provide the associated theory that is directly linked to the requirements in the clinic for healthcare decision making. We will focus on some of the most challenging public health problems of our time, including: Cancer, Cardiovascular Disease and Dementia. There is a core mathematical component that is common to data challenges across these clinical disciplines, which the hub will evaluate and address through our interdisciplinary collaborations. Creating the right environment for this approach to collaboration will be key to achieving our objectives. More specifically, our objectives for the hub are to: - Develop new mathematical techniques for optimally acquiring, analysing and processing imaging and non-imaging data in a joint fashion to improve patient stratification. - Close the gap between AI algorithmic capabilities and their lack of robustness and instabilities through rigorous mathematical treatment of AI techniques to prepare them for their application to sensitive decision making in the clinic. - Derive purposeful AI tools for clinical practice through a close collaboration between mathematicians, statisticians, computer scientists, biomedical researchers and clinicians. - Provide an online platform that hosts an open source modular toolkit and algorithm bank for an integrated analysis of patient data including clinical and multi-modal imaging data. - Contribute to the solution of eminent challenges in the diagnosis and treatment of cancer, mental and cardiovascular diseases through targeted research projects that develop mathematically sound AI techniques with clinical leads. These objectives are very closely related to the mathematical challenges our research at the hub will address: - Need for integrative data analysis methods for bringing imaging and non-imaging data (health record, genomics etc) together. - Need for minimal supervision methods to deal with the lack of clean and annotated data. Our work on bringing knowledge-driven analysis methods together with data-driven (supervised and unsupervised) approaches, that is learned regularisers (bilevel and deep neural networks), PDEs on graphs. - Need for holistic processing pipeline for jointly optimising the processing steps in the health data analysis pipeline. This will amount to highly non-convex, nonlinear and multi-objective optimisation problems in high dimensions. - Need for processing and analysis methods that are agnostic to the way the data have been collected and as such are robust to inter-site heterogeneities. This will require bringing together physics-based modelling with AI techniques for capturing the data acquisition step within this processing pipeline.

Research activity codes: 1.5 RESOURCES AND INFRASTRUCTURE (UNDERPINNING);4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: CANCER AND NEOPLASMS;GENERIC HEALTH RELEVANCE;NEUROLOGICAL