DRIVE-Health will train a minimum of 85 PhD health data scientists and engineers with the skills to deliver data-driven, personalised, sustainable healthcare for 2027 and beyond. Co-created with the NHS Trusts, healthcare providers, patients, healthtech, pharma, charities and health data stakeholders in the UK and internationally, it will build on the successes of its King's College London seed-funded and industry-leveraged pilot. Led by an established team, further growing the network of funding partners and collaborators built over the past four years, it will leverage an additional £1.45 of investment from King's and partners for every £1 invested by EPSRC. A CDT in data driven health is needed to deliver the EPSRC Priority for Transforming Health and Healthcare, EPSRC Health Technologies Strategy, and on challenges laid out in the UK Government's 2022 Plan for Digital Health and Social Care envisaging lifelong, joined-up health and care records, digitally-supported diagnoses and therapies, increasing access to NHS services through digital channels, and scaling up digital health self-help. This ambition is made possible by the increasing availability of real-world routine healthcare data (e.g. electronic health care record, prescriptions, scans) and non-healthcare sources (e.g. environmental, retail, insurance, consumer wearable devices) and the extraordinary advances in computational power and methods required to process it, which includes significant innovations in health informatics, data capture and curation, knowledge representation, machine learning and analytics. However, for these technological and data advances to deliver their full potential, we need to think imaginatively about how to re-engineer the processes, systems, and organisations that currently underpin the delivery of healthcare. We need to address challenges including transformation of the quality, speed and scale of multidisciplinary collaborations, and trusted systems that will facilitate adoption by people. This will require a new generation of scientists and engineers who combine technical knowledge with an understanding of how to design effective solutions and how to work with patients and professionals to deliver transformational change. DRIVE-Health's unique cohort-based doctoral research and training ecosystem, embedded across partner organisations, will equip students with specialist skills in five scientific themes co-produced with our partners and current students: (T1) Sustainable Healthcare Data Systems Engineering investigates methods and frameworks for developing scalable, integrated and secure data-driven software systems (T2) Multimodal Patient Data Streams will enable the vision of a highly heterogeneous data environment where device data from wearables, patient-generated content and structured/unstructured information from electronic health records can combine seamlessly (T3) Complex Simulations and Digital Twins focuses on the paradigm of building simulated environments, including healthcare settings or virtual patients, to make step-change advances in individual predictive models and to inform clinical and organisational decision-making. (T4) Trusted Next-Generation Clinical User Interfaces will place usability front and centre to ensure health data science applications are usable in clinical settings and are aligned with users' workflows (T5) Co-designing Impactful Healthcare Solutions, is a cross-cutting theme that ensures co-production and co-design in the context of health data science, engagement with stakeholders, evaluation techniques and achieving maximum impact. The theme training will be complemented with a cohort and programme-wide approach to personal, career, professional and leadership development. Students will be trained by an expert pool of 60+ supervisors from KCL and across partners, delivering outstanding supervision, student mentoring, opportunities, research quality and impact.

Tremendous amounts of data are produced every day in the life sciences and biomedicine and made available to the wider biological research community in the form of databases, data records and digitised information. The heterogeneity of these data, and at the same time, the potential amount of information that they can give us is astoundingly complex. We are at a crucial moment in biomedical research for which the effort of multiple disciplines in the quantitative and in the biomedical sciences have to come together to rationalise, quantify and extract the essential information content that can be translated into practical use in the medical context. We present a flexible training programme to facilitate the understanding of the data landscape that is populating modern medicine. More accurate and informed diagnoses will be possible if all the involved parties are able to extract useful information from patient data records and if these are efficiently integrated with genetic and analytical investigations with the aim of designing personalised therapies. The program is aimed at a large set of trainees: from medical practitioners, clinicians, scientists, companies and workers in the health sector. The courses will focus on skills training of different complexity that can be assembled in a personalised modular fashion. The flexibility is in the opportunity to pick and mix courses to generate learning curricula of different depth levels that can be started at any point. The offered courses will range from data exploration, integration and manipulation to more in depth analyses via computational statistical and artificial intelligence (AI) based methods. The trainees will have the opportunity to participate in the assembly of computational pipelines to analyse the data, and to bring to the table their own data for collaborative analyses. We have designed the training programme centred around three pillars (workstreams) that we believe are among our strengths in terms of training expertise, data collection and method development: Health Data Science exploring electronic data records (WS1); 'Omics harnessing genetics and molecular data collected in online databases (WS2); Artificial Intelligence focusing data image analysis and understanding AI through practical applications (WS3). The cross-talk between these areas of research is only at the beginning, this programme should facilitate collaborative efforts in identifying and overcoming the barriers for effective integration and translation across disciplines. The programme will engage the supporters, the patients and the public in workshops led by the participants sharing their learning experience and feedback suggestions for the structure and contents of the thought material. The program has three levels of governance: A) a management committee of PI, co-I's and WS leads; B) a stakeholder committee of representatives from academic research including ECRs, mid-career and senior leaders, clinical trainees and clinical academics and industry-based trainees; C) an advisory group with representatives invited from the funder and project partners to feedback information in a loop from which the program will continuously learn and improve.

The statistics for depression and anxiety in our young people are shocking. Over one-third experience these conditions and rates are rising, particularly in young women. Anxiety and depressive disorders are highly debilitating, disrupt education, reduce normal work capacity and dramatically increase suicide risk. Despite this, <£2 is spent per person per year on research into understanding them. Anxiety and depression also have very complex relationships with physical health conditions, with growing evidence for bidirectional effects, and putative sub-types of depression with specific physical health profiles. This complex picture is made even worse by the stigma which still surrounds these conditions, so many people do not seek help, or if they do, they do so for physical rather than psychological concerns. This backdrop means we know much less than we need to about how anxiety and depression develop, who is most at risk, when and how these conditions influence and are influenced by physical health concerns, and which factors drive treatment seeking and more general health service use. Furthermore, despite having known for centuries that anxiety and depression "run in families" we know very little about which factors lead to the child of a parent with anxiety or depression developing that condition themselves. This question is of key importance to many young people experiencing anxiety and depression. Our overarching aim is to transform our ability to predict who is at risk of anxiety and/or depression in their mid-twenties and our understanding of how related traits are transmitted from one generation to the next. Our findings will allow us to specify for whom and when to intervene to disrupt the development and intergenerational cycle of these conditions. To address this aim, we will undertake three sets of new data collection with participants of the Twins Early Development Study (TEDS). TEDS has followed twins born in England and Wales in 1994-1996 from birth, assessing a wide array of emotional, behavioural, cognitive and language measures. Genome-wide genetic data are also available. Approximately 10,000 families are still active in the study, of whom ~65% consistently respond at each wave of data collection. As they approach their mid-twenties the twins are starting to have children, providing an exciting and unique opportunity to re-engage them and their offspring. First, we will collect information about current mental health conditions using online assessment at age 26. This will allow us to utilise all our prior information to build models that identify groups at the greatest risk of developing mental health conditions in young adulthood, who could benefit from early prevention efforts. Second, we will connect information from TEDS twins' routine medical records to our dataset, built up over 25 years. This will offer additional external, independent information, including on mental health conditions, physical health conditions and use of medical services, all of which can help refine models of risk. Third, we will recruit and assess the children of TEDS participants, which will allow estimation, beyond the relative contributions of genes and environment, of parent-to-child and child-to-parent effects. We will use this work to drive a new wave of prevention trials, built on the risk models we devise. Furthermore, we will continue to encourage researchers internationally to access the TEDS data resource to address questions beyond our core focus.