About the Project
Life-spans are increasing globally and the worldwide number of people with age-related dementia is expected to reach 75 million by 2030. Improving the early identification of people at risk of developing dementia is a major priority for the NHS, in order that interventions to slow or stop the disease can be started at an early stage. Dementia is also a priority research area at Manchester University where a pan-University organisation dementia@manchester has been established to bring together the extensive community of dementia researchers, clinicians and regional care providers.
This study will build on ongoing collaborative work at Manchester and at Goldsmiths, University of London, using large-scale datasets of electronic patient records and other data sources to identify patient factors associated with the development of dementia and to build predictive algorithms using cutting-edge statistical and machine-learning methods. Existing predictive models have demonstrated only modest performance and the goal is to substantially improve on these by making better use of the longitudinal historical information in the patient record and by combining health, genetic and possibly brain imaging data. The student will join a thriving multi-disciplinary group of researchers at Manchester working in the areas of statistics, predictive models, dementia and health informatics and will also be linked in with our collaborators at the Computing Department at Goldsmiths, led by Dr Daniel Stamate. Training in relevant methods will be provided.
At the end of the PhD you will have developed expertise in managing and analysing large-scale health databases, in advanced statistical and machine-learning techniques and their application to predictive modelling, and in the use of sophisticated statistical software packages such as Stata and R. You will also have developed skills in working within a multi-disciplinary team and in academic writing, presentation, and publishing.
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in statistics or a related area. It would be an advantage for candidates to have experience of applying statistics or informatics in a health-related area.
For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Bioinformatics.
For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit www.internationalphd.manchester.ac.uk
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website View Website
Ford, E., Rooney, P., Oliver, S. et al. Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches. BMC Med Inform Decis Mak 19, 248 (2019). https://doi.org/10.1186/s12911-019-0991-9
Reeves D, Springate DA, Ashcroft DM, Ryan R, Doran T, et al. Can analyses of electronic patient records be independently and externally validated? The effect of statins on the mortality of patients with Ischaemic heart disease: a cohort study with nested case-control analysis. BMJ Open 2014;4:4:e004952 doi: 10.1136/bmjopen-2014-004952
Stocks, S.J., Kontopantelis, E., Webb, R.T…., Ashcroft DM. Antipsychotic Prescribing to Patients Diagnosed with Dementia Without a Diagnosis of Psychosis in the Context of National Guidance and Drug Safety Warnings: Longitudinal Study in UK General Practice. Drug Saf 40, 679–692 (2017). https://doi.org/10.1007/s40264-017-0538-x
Stamate D. et al. (2020) Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 584. Springer, Cham. https://doi.org/10.1007/978-3-030-49186-4_26
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