Active Computing & AI Psychology & Behaviour

Big Data for Complex Disease

In plain English

AI plain-English summary

The programme will link health records, genetic data, and other information from the entire UK population—roughly 60 to 65 million people—to study how complex diseases such as cancer and cardiovascular disease interact and affect patient outcomes. These two disease groups cause the majority of illness and death in the UK and globally. Yet researchers typically study them in isolation, missing how one condition influences another or how a patient’s characteristics—age, genetics, lifestyle—alter their risk and response to treatment. This project fills that gap by bringing together experts across disease types and data sources to see the full picture. If successful, the work could transform how the NHS predicts, diagnoses, and treats complex illness. Instead of treating cancer and heart disease separately, clinicians might spot shared risk factors earlier, tailor treatments to individual patients, or even prevent one disease by managing another. The tools and insights developed here could quietly reshape medical decision-making at scale, improving care for millions without most people ever noticing the infrastructure behind it.

View original technical description
Big Data for Complex Disease focuses on bringing together different types of information about patients to better understand opportunities to improve health care through the prediction, diagnosis, treatment or even prevention of complex disease. The work will initially focus on cancer and cardiovascular disease (CVD) the complex diseases responsible for the majority of disease and death in the UK and globally (CVD includes a range of disease such as heart attacks, strokes and Arrhythmia). This programme will use data from the whole UK population (60-65 million people), providing new tools and opportunities to understand how complex diseases effect each other and how peoples characteristics impact the chance of getting and outcomes of these diseases. This challenge will be an opportunity to bring experts in these different diseases types together and work as a team to improve patient care.

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Researchers

Cathie Sudlow (Principal Investigator)Mark Lawler (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Big Data: Little Disease
Predictive analytics of integrated genomic and clinical data using machine learning and complex statistical approaches
Evaluating effects of complex treatments using large observational datasets: from population to person
Exploiting the protein-protein interaction network to identify common genetic variants associated with complex diseases
Using data and computational infrastructure to understand multi-morbidity effect and social factors on haematological cancer incidence and outcomes

Original classification

Intramural

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.