Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Recent advances in medicine and health policy have led to an unprecedented increase in life expectancy. This longevity has also seen an increasing number of people, particularly older adults, suffering from two or more serious chronic diseases - a status known as multimorbidity.


While many demands are made of the sustainable health agenda, this burden of multimorbidity looms large. In the European Union alone, a paper from 2013 estimated that 50 million people are affected by co-existing diseases, and this figure is expected to rise over the next 20 years. Despite this, many questions around multimorbidity remain unanswered.

Other than age, little is known about the factors driving the trend towards more people living with multiple chronic conditions. Most health-related research is focused on the prevention and management of single medical conditions in isolation, which makes it difficult to develop the evidence-based strategies that patients and healthcare systems crucially need to understand the extent of this burden, and to most effectively treat disease clusters and interactions.

In order to better understand these challenges and to provide solutions, our interdisciplinary research team translates health challenges (such as disease clusters, risk trajectories and treatment effects) into machine learning problems. This translation involves applying data mining and deep learning methods (proven to be more effective than many existent statistical models) to some of the world’s largest biomedical datasets comprising information on the health status, treatment histories and outcomes of millions of individuals.

Findings from this research will help us understand how multiple chronic disease profiles are evaluated, managed, and treated, and bolster strategies that either prevent their occurrence in the first place, including looking how diseases are linked, or support critical interventions. Clinicians and health service providers will be better able to pinpoint which groups are likely to benefit most from treatment, and what factors could be contributing to poor health, in order to improve quality of life and survival rates.

The urgent need for such research was underscored by the Academy of Medical Sciences’ 2018 policy report on multimorbidity, chaired by our Principal Director, Professor Stephen MacMahon. The National Institute for Health Research Oxford Biomedical Centre also has a theme focused on Multimorbidity and Long-Term Conditions, within which Professor Kazem Rahimi is an investigator.

Our team

  • Kazem Rahimi
    Kazem Rahimi

    Professor of Cardiovascular Medicine and Population Health

  • Shishir Rao
    Shishir Rao

    Postdoctoral Researcher in Machine Learning for Cardiovascular Epidemiology

Related research themes