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  Predicting harm from prescribed drugs in people with polypharmacy, multimorbidity and frailty

   College of Medicine and Veterinary Medicine

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  Prof Bruce Guthrie, Prof Jacques Fleuriot  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

The Advanced Care Research Centre (ACRC) is an interdisciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”.

This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the ACRC, whose students will deliver key aspects of the ACRC research agenda through a doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors.

The PhD with Integrated Study in Advanced Care is a structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business, social work, medicine and related health and care professions. This unique level of interdisciplinarity is a key attribute of our programme.



The aim of this project is to develop and validate new models to predict who is at risk of being harmed by prescribed drugs, focusing on the outcome of acute kidney injury.


  • To systematically review the literature examining medication and other causes of acute kidney injury (AKI)
  • To apply and compare epidemiological and machine learning approaches to predicting AKI risk associated with medication and other patient characteristics


Acute kidney injury is common and associated with multiple longer-term adverse outcomes. Medication is an important preventable cause of AKI, but our understanding of how prescribing interacts with underlying conditions and other patient characteristics like frailty is poor. This project will use large-scale routine healthcare data to develop and validate prediction models for AKI, and to compare models based on different approaches in terms of performance (discrimination, calibration), explainability, and feasibility to apply in live clinical data. There will be flexibility for the student to develop a focus and choice of methods that suits their own interests.


A relevant undergraduate or masters degree with machine learning, quantitative analysis or statistics training (eg informatics, computer science, Q-step social science, epidemiology, biostatistics, biomedical sciences).

We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application. 

The Academy aims to foster a supportive and collaborative culture, and welcomes candidates with diverse backgrounds and experiences.

You must read How to apply prior to application

Please Apply here

Computer Science (8) Mathematics (25) Medicine (26)

Funding Notes

PhDs are funded with an enhanced stipend for the full 4 year period
The call is open to candidates of any nationality but funded places for candidates with international fees status are limited.
It is essential to read the How to Apply section of our website before you apply:
Please apply here:

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