牛津大学招收卫生信息学博士
About the Project
The World Health Organisation recognises climate change as the greatest threat to human health. It affects social and environmental determinants of health and compounds underlying intersectional health inequalities. Mitigation and adaptation planning is considered to have health and climate co-benefits. At the interdisciplinary Planetary Health Informatics Lab we study how artificial intelligence (AI) and large environmental, climate and health datasets can be used to generate evidence and technological solutions for equitable climate-health mitigation and planning.Large routinely collected observational health data, coupled with earth observation data and robust artificial intelligence approaches can fill critical evidence gaps and support more informed healthcare and policy decisions, ultimately improving health outcomes for everyone.
The successful candidate, for this 3-year DPhil project will have access to real world data such as electronic medical records, data on social determinants of health (e.g. UK Biobank) and environmental data through earth observation and satellite databases. They will have an opportunity to use epidemiological, statistical, and artificial intelligence methods to undertake this climate-health data science study. Our team has studied electronic health records for over 250 ethnicity groups from >60 million individuals in the UK. We have examined international climate and health trends over the last 30 years using earth observation data.
This DPhil project will bring together climate and health datasets to generate evidence from real world data to describe, analyse and develop climate-health prediction models for most at-risk climate-vulnerable communities. The project can involve a variety of data curation, descriptive analysis, and prediction modelling techniques. Questions that may be of interest can include but not be limited to: 1) what are the impacts of environmental changes on human health, 2) what are the impacts of extreme weather events and natural disasters on human health, and 3) how can we develop early warning systems to help communities, authorities, and healthcare services to plan for these planetary health changes. To achieve this, the student will have access to a range of secure data sources including but not limited to Clinical Practice Research Datalink (CPRD), Health Data Research UK (HDRUK) Trusted Research Environment, UKBiobank, international health datasets, and satellite imagery data bases e.g. Landsat and Sentinel.
The student will be expected to bring their own ideas to enrich the study.
TRAINING OPPORTUNITIES
The PhD student will receive training, support and supervision from academic supervisors in a top world class academic environment at the Centre for Statistics in Medicine and the Big Data Institute at the University of Oxford. The student will be part of an established research group, the Planetary Health Informatics Lab with access to data, state-of-the-art high-performance computational resources and facilities, experience in the proposed research. They will join an exciting group of over >20 staff and 10 PhD and MSc students.
The group will ensure hands-on training in real world data science and artificial intelligence using health and environment data. The student will work on their unique project within an experienced and collaborative supervisory team. The student would be supported to attend relevant conferences to enrich their studies and financial support will be made available for travel to conferences.
Alongside with the opportunities listed above, the department offers extra training:
The Big Data Institute (BDI) is an interdisciplinary research institute that focuses on the analysis of large, complex, heterogeneous data sets for research into the causes and consequences, prevention and treatment of disease.
The Centre for Statistics in Medicine is committed to improving the standard of health research through research and training on research and methods development, and home to the UK Equator Centre. This enables and encourages research and education to champion transparent and complete reporting of health research through reporting guidelines and training provision. A core curriculum of lectures will be taken to provide a solid foundation in a broad range of subjects including statistics, epidemiology, and big data analysis. All students will be required to attend a 2-day Statistical and Experimental Design course at NDORMS and the Real World Epidemiology: Oxford Summer School. Students will also be required to attend regular seminars within the Department and have access to a variety of other courses run by the Medical Sciences Division Skills Training Team and the wider University, such as the UK Equator Publication School. Finally, the student(s) will be expected to regularly present data in Departmental seminars, collaborators across the University and internationally.
KEYWORDS: Real-world data, health disparities
HOW TO APPLY
It is recommended that, in the first instance, you contact the relevant supervisor(s) and the Graduate Studies Office (graduate.studies@ndorms.ox.ac.uk) who will be able to advise you of the essential requirements.
Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable).
The application guide and form is found online and the DPhil will commence in October 2024.
Applications should be made to the following programme using the specified course code:
• DPhil in Clinical Epidemiology and Medical Statistics (course code: RD_NNRA1)
For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.
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