The Oxford Martin Programme on

Deep Medicine

The Challenge

Combining expertise in healthcare, epidemiology, computer science and engineering, we are investigating how application of advanced statistics, machine learning and deep learning techniques to large and complex biomedical datasets can lead to better understanding and management of chronic diseases and multimorbidity.

Advances in medicine over the past few decades have led to an unprecedented increase in life expectancy and reduction in major disabilities. More people can expect to live into their 60s and beyond than die prematurely before the age of 60. But this longevity brings with it new problems: a rise in chronic conditions such as diabetes, dementia and heart failure, with many patients suffering from multiple conditions at the same time (multimorbidity), which are poorly understood.

Our limited understanding of complex disease patterns and risks is further compounded by current methods of research, which tend to oversimplify complexity and often underrepresent patients with complicated disease history or concurrent treatments.

The advent of ‘Big Data’ can offer unprecedented opportunities for extending our knowledge and understanding of complex disease patterns and risk, but harnessing its full potential will require new and innovative methods of analysis.

We are developing scalable methods for analysis of biomedical datasets, aim to generate insights into complex disease patterns, risk trajectories and treatment effects, and provide empirical evidence for the value of applying data mining, modelling and machine learning techniques to biomedical data.

Priority areas include the development and testing of models that make use of the depth and breadth of electronic health records to better represent an individual’s medical history. Methods are being developed to assess causal questions and uncertainty of estimations in the context of complex observational data and to contrast them with conventional epidemiological methods, including survival models, Mendelian randomisation and clinical trials.

We seek collaborations with other international partners in both academia and commercial sector for further validation and extension of our work.

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Lowering blood pressure is still beneficial for the heart in old age

Blood pressure medication could lower heart attack and stroke risk even when blood pressure is not substantially raised

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Many more people could benefit from blood pressure lowering medication

The Lancet has published new research showing that blood pressure-lowering medication can prevent serious cardiovascular conditions such as strokes, heart failure and heart attacks even in adults with normal blood pressure.

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Meta-analysis shows no link between blood pressure medications and cancer risk

A meta-analysis involving over 260,000 participants from 33 randomised controlled trials has sought to resolve the long-debated issue about whether using antihypertensive medication heightens the chance of developing cancer.

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Biobank data reveal long-term exposure to traffic noise may impact weight gain in the UK population

Transport noise is a major problem in Europe, with over 100 million people living in areas where road traffic noise exceeds levels greater than 55dB, the health-based threshold set by the EU.

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Using the Power of Deep Learning for Clinical Risk Prediction

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Heart failure care must address broader health to improve survival rates, says study

Research published in JAMA Cardiology today presents new evidence that might explain why the prognosis of heart failure patients has improved so little over the past decade.

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