The Advanced Care Research Centre (ACRC) is a new, multi-disciplinary, £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 new 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 novel, 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 and commerce, social work, medicine and related health and care professions. This unique level of diversity is a key attribute of our programme.
This project will develop novel machine learning models in survey and routine data for studying trajectories of multimorbidity to better predict adverse events and development of further conditions in later life, and clustering individual trajectories in meaningful groups to better operationalize them in clinical context.
- Develop interpretable statistical and machine learning models, e.g., using deep learning, mixture models, stochastic processes, etc. for modelling and clustering trajectories of multimorbidity in a principled manner
- Assess the performance of these models in predicting adverse outcomes and development of further conditions in later life
- Find stable clusters of multimorbidity trajectories in routine (CPRD) and survey (ELSA) data, and interpret these clusters
- Compare resulting clusters against clusters derived from existing approaches to assess their consistency, consensus and replicability
Multimorbidity is defined as the co-existence of two or more chronic conditions and is associated with a wide range of adverse outcomes in later life. The pattern of multimorbidity experienced by individuals vary widely and clustering them in meaningful groups has received considerable interest [1, 2, 3] in both routine and survey data. This is a difficult problem due to the inherent complexity of modelling multivariate trajectories with limited quality data. This project aims to explore this area by developing novel machine learning methods to model these trajectories and find clusters that are stable, consistent, reproducible, fair, explainable, and operationalizable. The project will explore both data-driven methods, e.g., deep learning with attention mechanism, and model-driven methods e.g., mixture of stochastic processes.
The project is suitable for candidates with 1) strong background in statistics, machine learning or related quantitative field, 2) strong interest in the application of these tools in health and care, 3) strong interest in working within an interdisciplinary team, and 4) experience in dealing with health and survey data.
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.
You must read How to apply prior to application
Please Apply here