Fully funded PhD on 'Artificial Intelligence in Modelling the Influence of Socio-Economic Factors on the Risk of Cardiovascular Events'
Deadline: 07 January 2021
Barriers to education, housing, and a low income are associated with a high prevalence of cardiovascular risk factors and a greater risk of developing serious cardiac problems or die prematurely. Therefore, improving socio-economic factors might lead to better health and less health inequalities.
We are a large multidisciplinary team of researchers, including experts in machine learning, cardiologists and statisticians. For this project, we will use a large set of anonymised healthcare records, such as blood tests, clinical appointments, and investigations that are routinely collected, every day in the West of Scotland. Advanced machine learning techniques will be developed to model interdependencies in longitudinal EHR and predict key adverse clinical events.
We will investigate the relationship between socio-economic factors and the incidence and outcome of severe cardiovascular disease, including strokes, heart attacks, atrial fibrillation and heart failure This information will help doctors to target interventions that might prevent the onset or improve the outcome of serious cardiovascular disease, especially for those at the greatest socio-economic disadvantage.
The successful candidate is required to have prior knowledge in Machine Learning and/or a degree in related field.
For more details email Fani Deligianni [log in to unmask] and send a copy of your CV.
Best Regards,
Fani Deligianni
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Fani Deligianni, PhD
Lecturer,
321b Sir Alwyn Williams Building,
School of Computing Science,
Glasgow University
https://www.gla.ac.uk/schools/computing/staff/fanideligianni/
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