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 Innovating Across Sectors (IaS) research program examines the challenges of partnering across sectors (cross-sectoral partnerships) to provide public goods, such as healthcare. IaS research focuses on newly-formed partnerships between the public sector (government services and systems) and the third sector (i.e. community, nonprofit organizations, open source networks and databanks). IaS further focuses on examining those partnerships where new digital technologies (platforms, social media, artificial intelligence), often created by the private sector, have a prominent role. The challenge of creating, maintaining, managing and regulating these partnerships is heightened because they involve multiple stakeholders (service users such as patients, professionals and professional associations, private companies, organizational leaders, policymakers and the public). IaS leverages an interdisciplinary research team, as well as both qualitative and quantitative research methods, to examine in real-life settings a series of cases in the UK and Canada with plans to expand to the USA and more countries. We aim to address important questions of how these new innovative partnerships emerge, how they can be sustainable, to understand their intended and unintended consequences for various stakeholders and how these are regulated or managed in real-life settings.

Many aspects of a child or young person's life can affect their mental health. If someone has a serious mental health problem their general practitioner (GP) may refer them to mental health (psychiatry) services for assessment and treatment by professionals. Mental health services are stretched so often intervene late, leaving people to suffer unnecessarily with problems that therefore may last longer, be more severe, or be harder to treat. Early warning signs of mental health problems may be noticed by the person themselves or by others (e.g. school staff, social workers). Many things can suggest a mental health problem, such as difficult early experiences, bullying, changes in behaviour, poor school attendance or grades, or risk-taking. Not all who experience one or more of these will have a mental health problem, so we need to take them together to spot patterns that show who is developing problems and may need professional help. However, this information (data) is stored in different places, e.g. by schools, GPs and social workers and so it may be impossible to spot problems early. Some researchers have joined data from two or more sources to find patterns suggesting mental health problems. Their success indicates good potential in this approach, but they have not made a practical difference for two main reasons: 1) the models are not yet accurate enough, probably because they omit many factors that can lead to problems; 2) the results cannot be used directly to help young people as they are based on anonymous data. We will develop a system that can be used by health, education, or social workers to identify adolescents showing early signs of mental health problems, to offer them help sooner. At the same time we want to provide better anonymous data for research into predicting mental health problems. Data must be held securely (most likely in the NHS), and only people involved in a person's care should be able to see it, but we need to understand how best to do this. To use data for research while protecting privacy it will be anonymised, removing anything that directly identifies a person (e.g. name, address, date of birth, NHS number) and access will be restricted to approved researchers. But we do not yet know what technical problems there may be in linking the databases, or what data the system will need in order to detect people showing early signs of a problem. The final challenge is how to make this work within the NHS, schools, and social care settings to enable earlier identification of young sufferers of mental health problems. Over the next year, we want to tackle these challenges by creating a group including mental health researchers, psychologists, schools, the NHS, councils, computer scientists, security experts, mathematicians, people who provide services, and policy makers, many of whom are doing ground-breaking work in other areas. We want to turn their attention to jointly solving these problems. We must involve young people, their carers, and people with lived experience: it is their data and we need to understand their views. We would like their help thinking about which professionals can see their data, and what should happen when a young person is thought to be developing mental health problems. We will hold workshops about these questions. We also have permission to create an initial data set with data from health, social services, and education. We will anonymise these, and practise linking and analysing them. These will help us understand the challenges, so that our final plan will be more detailed and likely to succeed. In the future we want to test if a computer program makes it easier to identify mental health problems and offer young people treatments earlier, and if they get better quicker because of this. This might have a range of benefits including helping with school, relationships, home life, and getting jobs or into university, and we want to test this theory.

One in ten children in the UK are affected by a mental health problem, causing significant distress to them and their families. Where these problems endure, they can hold children back from reaching their potential in school and the workplace, and from experiencing good physical and mental health into adulthood. Financially, the personal cost of mental illness is £41.8 billion per year in England. In light of this burden to children, families, and society, there is a pressing need for a pathway that can prevent mental health problems as early as possible. We now know that many of the factors that shape risk and resilience to mental health problems have their roots in the first years of life. Children who start off more vulnerable can go on to develop initial difficulties, which can then progress into more established problems. Developing better ways to identify which children and families are likely to benefit from support would help professionals to work with families to take a proactive approach early on. By supporting families to provide responsive, consistent care, we can help to build a strong foundation for mental health. Doing this in the first years of life, when children's development is especially responsive to their early experiences, relationships, and environment, could unlock huge potential to shape the course of children's long-term mental health. Research also suggests that investing early makes economic sense as children are less likely to need more intensive supports later on in life. This promise of a strong start in life has made children's first 1001 days a global health priority, as reflected in the recent World Health Organisation 'Nurturing Care' framework for early childhood development. Yet the insights we have from decades of research in child development have not translated into the public health strategies we need to promote early mental health in the UK. There are two critical factors underlying this gap. Firstly, we lack a way to identify early risk and resilience for mental health problems in very young children that is quick, effective and acceptable to families and professionals. Secondly, early childhood programmes that show promise in preventing problems when they are tested in controlled research studies typically fail to show the same success when they are delivered in real-world services. Although these programmes have been carefully developed they are often too complicated and expensive to deliver at scale. This fellowship will use cutting-edge techniques in epidemiology and data science to develop a tool to identify early mental health needs in very young children and a pathway for more personalised supports. It will bring together the best evidence available from previous studies of early interventions so we can identify which practices and strategies in these programmes tend to be most effective. Stripping these programmes back to their most important building blocks will allow us to work together with families and professionals to redesign how they are delivered so they fit better into family life, respond to families' needs and priorities, and are feasible and practical to deliver. This will be done by testing different approaches out quickly, figuring out what does and doesn't work, and adapting the approach based on this learning. We will do this in the UK as well as undertaking initial piloting in South Africa to ensure the principles and approaches we develop are flexible and can be adapted appropriately to different resource and cultural contexts. The ultimate goal of this research is to co-develop a flexible prevention pathway for early mental health problems that is relevant to the challenges facing families and communities and is responsive to the needs of family life and the services in which they are delivered. This research has the potential to provide the breakthrough impacts needed to change the course of children's mental health.