EPSRC Centre for Doctoral Training in Health Data Science

Lead Research Organisation: University of Oxford
Department Name: Computer Science

Abstract

Data science and artificial intelligence will transform the way in which we live and work, creating new opportunities and challenges to which we must respond. Some of the greatest opportunities lie in the field of human health, where data science can help us to predict and diagnose disease, determine the effectiveness of existing treatments, and improve the quality and affordability of care.

The Oxford EPSRC CDT in Health Data Science will provide training in:
- core data science principles and techniques, drawing upon expertise in computer science, statistics, and engineering
- the interpretation and analysis of different kinds of health data, drawing upon expertise in genomics, imaging, and sensors
- the methodology and practice of health data research, drawing upon expertise in population health, epidemiology, and research ethics

The training will be provided by academics from five university departments, working together to provide a coordinated programme of collaborative learning, practical experience, and research supervision.

The CDT will be based in the Oxford Big Data Institute (BDI), a hub for multi-disciplinary research at the heart of the University's medical campus. A large area on the lower ground floor of the BDI building will be allocated to the CDT. This area will be refurbished to provide study space for the students, and dedicated teaching space for classes, workshops, group exercises, and presentations.

Oxford University Hospitals NHS Foundation Trust (OUH), one of the largest teaching hospitals in the UK, will provide access to real-world clinical and laboratory data for training and research purposes. OUH will provide also access to expertise in clinical informatics and data governance, from a practical NHS perspective. This will help students to develop a deep understanding of health data and the mechanisms of healthcare delivery.

Industrial partners - healthcare technology and pharmaceutical companies - will contribute to the training in other ways: helping to develop research proposals; participating in data challenges and workshops; and offering placements and internships. This will help students to develop a deep understanding of how scientific research can be translated into business innovation and value.

The Ethox Centre, also based within the BDI building, will provide training in research ethics at every stage of the programme, and the EPSRC ORBIT team will provide training in responsible research and innovation. Ethics and research responsibility are central to health data science, and the CDT will aim to play a leading role in developing and demonstrating ethical, responsible research practices.

The CDT will work closely with national initiatives in data science and health data research, including the ATI and HDR UK. Through these initiatives, students will be able to interact with researchers from a wide network of collaborating organisations, including students from other CDTs. There will also be opportunities for student exchanges with international partners, including the Berlin Big Data Centre.

Students graduating from the CDT will be able to understand and explore complex health datasets, helping others to ask questions of the data, and to interpret the results. They will be able to develop the new algorithms, methods, and tools that are required. They will be able to create explanatory and predictive models for disease, helping to inform treatment decisions and health policy.

The emphasis upon 'team science' and multi-disciplinary working will help to ensure that our students have a lasting, positive impact beyond their own work, delivering value for the organisations that they join and for the whole health data science community.

Planned Impact

In the same way that bioinformatics has transformed genomic research and clinical practice, health data science will have a dramatic and lasting impact upon the broader fields of medical research, population health, and healthcare delivery. The beneficiaries of the proposed training programme, and of the research that it delivers and enables, will include academia, industry, healthcare, and the broader UK economy.

Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.

Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.

Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.

Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S02428X/1 01/04/2019 30/09/2027
2279638 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Jonathan Oliver Campbell
2282001 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Kangning Zhang
2281987 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Hang Yuan
2279808 Studentship EP/S02428X/1 01/10/2019 30/12/2023 Claudia Vanea
2280681 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Maxime Guillaume Kayser
2271697 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Henrique Rui Aguiar
2280532 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Cornelius Emde
2280883 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Xinchi Qiu
2279748 Studentship EP/S02428X/1 01/10/2019 30/12/2023 Odhran Richard O'Donoghue
2279625 Studentship EP/S02428X/1 01/10/2019 30/12/2023 Jacob Armstrong
2279773 Studentship EP/S02428X/1 01/10/2019 30/09/2023 Andres Tamm
2432736 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Angus James Nicolson
2432652 Studentship EP/S02428X/1 01/10/2020 30/09/2024 George Henry Batchkala
2432289 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Alexander Sauer
2431966 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Adam Sturge
2432658 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Owen Patrick Dwyer
2431522 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Jong Hwan Kwon
2432401 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Ruby Wood
2432026 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Lara Chammas
2432761 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Kan Keeratimahat
2432020 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Niall Taylor
2431819 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Aleksandra Krepa
2294883 Studentship EP/S02428X/1 01/10/2020 30/09/2024 Harriet Longley
2593947 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Nasma Dasser
2592417 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Ambre Bertrand
2599185 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Abram Schonfeldt
2593955 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Ruben Weitzman
2593917 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Isobel Howard
2594573 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Felix Karl Wagner
2593890 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Eloise Ockenden
2599168 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Jiazheng Zhu
2594554 Studentship EP/S02428X/1 01/10/2021 30/09/2025 Lav Radosavljevic