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Research project: Developing a Multidisciplinary Ecosystem to study Lifecourse Determinants of Complex Mid-life Multimorbidity using Artificial Intelligence (MELD)

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This study is developing the environment, systems and methods to allow artificial intelligence techniques to ‘connect’ birth cohort data with large GP datasets.

MELD study
MELD study

As with many countries we are facing challenges related to the growing number of people living with multiple long-term health conditions like diabetes, heart disease or dementia. All the way through peoples’ lives many things influence the chances of developing such conditions: things about people themselves, such as age and ethnicity, things that happen like infections or accidents, and behaviours like smoking and diet.

Perhaps even more important, though hard to research, are broader issues throughout life such as the environment people grew up in, their education, work, income and so on. The uncomfortable truth is that people from more disadvantaged backgrounds are more likely to develop multiple conditions at an earlier age. There is also evidence that the order of developing conditions varies considerably and influences what then happens to people. This makes understanding these broader issues and how they affect that order vital to inform when and how we should intervene.

To achieve this, we need to study exceptionally large numbers of people over their whole lifetime, but such datasets do not exist.

The aim of this research is to safely and ethically establish the necessary environment, systems and methods to allow artificial intelligence techniques to ‘connect’ birth cohort data with large GP datasets. This will allow us to connect information on the broader, lifecourse issues with the GP information on long-term conditions. Then we can:

1. Identify the kinds of people who develop combinations of burdensome long-term conditions by the time they are middle-aged.
2. Understand the order of developing long-term conditions through life and which ones develop first.
3. Work out how broader issues affect that order and the resulting combination of conditions.

Methods which will be used in this study include:

  • Clustering methods
  • Causal inference methods
  • AI transfer learning methods

Potential impact
Our vision is to scale the MELD ecosystem using other birth cohorts and routine datasets to explore the lifecourse relationship between sequence of exposure to wider determinants, sentinel and subsequent clinical events, and development of complex multiple long-term health condition clusters.

Funder: NIHR

Duration: Start date 4th January 2021, End date 5th September 2021


Related research groups

Primary Care, Population Sciences and Medical Education
Population Health Sciences Research group
Applied Mathematics and Theoretical Physics
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