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What can data tell us about our frailty?


School of Informatics

Edinburgh United Kingdom Applied Statistics Data Analysis Epidemiology Other Statistics

About the Project

The Advanced Care Research Centre at the University of Edinburgh is a new £20m interdisciplinary research collaboration aiming to transform later life with person centred integrated care

The vision of the ACRC is to play a vital role in addressing the Grand Challenge of ageing by transformational research that will support the functional ability of people in later life so they can contribute to their own welfare for longer. With fresh and diverse thinking across interdisciplinary perspectives our academy students will work to creatively embed deep understanding, data science, artificial intelligence, assistive technologies and robotics into systems of health and social care supporting the independence, dignity and quality-of-life of people living in their own homes and in supported care environments.

The ACRC Academy will equip future leaders to drive society’s response to the challenges of later life care provision; a problem which is growing in scale, complexity and urgency. Our alumni will become leaders in across a diverse range of pioneering and influential roles in the public, private and third sectors.

Frailty is a key concept in population ageing and geriatric clinical practice. Whilst specific definitions and measures of frailty are contested, there is general agreement that frailty is a non-specific state reflecting age-related declines in multiple systems, leading to a range of adverse outcomes such as falls, fractures, hospitalisation, institutionalisation and mortality, and a version of this (the eFI) is already used by GPs across the UK to screen for frailty and target health and social care interventions in the community. Existing frailly indices are defined as a cumulative deficit model, i.e., they count the accrual of health issues or 'deficits' but do not distinguish deficits that underlie a frailty score. The goal of this project is to investigate novel definitions of frailty using statistical machine learning with the objective of conceptualising frailty as a, potentially complex and multidimensional, latent construct that varies across social, economic, demographic groups and birth cohorts, and assessing whether these bring improved capacity to predict adverse outcomes in survey (English Longitudinal Study of Ageing) and routine data (DataLoch).

Specifically, the project will:

  • Explore and compare the structure of frailty in routine and administrative data,
  • Define frailty as a (potentially multidimensional) latent construct based on probabilistic modelling of observed deficits, and/or based on encoder-decoder system that relates deficits to adverse outcomes,
  • Explore the usefulness of existing and novel frailty measures in the context of categorising patients in the community and when admitted to hospital,
  • Validate existing and novel frailty measures using similar survey and routine data across the world.

The project will be part of the ACRC workpackage on 'data-driven insight and prediction', and it will align with several other workpackages for finding frailty from text (enhancing the data infrastructure), for validating frailty in sensing platform (new technologies of care), and for validating frailty in practice (new models of care). The project will be supervised by an interdisciplinary team of academics from three colleges with expertise in machine learning (Sohan Seth, CSE), geriatric practices (Atul Anand, CMVM) and social policy (Alan Marshall, CAHSS).


Funding Notes

PhD's are fully funded with an above industry stipend for the full 4 year period.

The call is open to candidates of any nationality but funded places for overseas nationals will be strictly limited to 3 international students who can apply for the highly competitive ACRC Global Scholarship.

Application forms are now available here:
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Find more information on how to apply on the How to Apply section of our website:
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References

ACRC Academy Video:


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