National Projects
Five UK-wide research projects are creating the tools and technologies needed to generate new knowledge from health datasets.
These national implementation projects are led by teams of researchers, scientists and technologists from Health Data Research UK鈥檚 sites and will develop innovative solutions to tackle some of the UK鈥檚 biggest health challenges including stroke, lung cancer, mental illness and multi-morbidity.
Each project champions work in support of our mission to unite the UK鈥檚 health data to make discoveries that improve people鈥檚 live and bring together colleagues across our six sites who have diverse research interests and a common vision.
Following a rigorous selection process, which involved matchmaking projects with similar aims, the projects are underway and are expected to complete by March 2023 or sooner.
Find out more about the projects below.
National Multimorbidity Resource
This project will work with researchers across the UK bringing together 6 different datasets which hold anonymised information on over 10 million people to explore multimorbidity in the UK at a...
National Phenomics Resource
We will develop new tools to help analyse Electronic Health Records (EHRs) and will help to build a community among users of these data, who will be encouraged to share their methods. This will...
National Text Analytics Resource
When you visit your doctor or attend hospital, information collected about you, including your symptoms, tests, investigations, diagnosis, and treatments, is entered on computers as electronic...
A national multi-omics consortium to inform disease aetiology and prediction
Overview Genetics has transformed our understanding of how variation in DNA can influence risk of developing conditions, such as cancer and heart disease. Studies that can combine this genetic...
Reproducible Machine Learning in Health Data Science
51爆料网 Site Multiple Project Description Four key challenges will be investigated to improve the trustworthiness of machine learning in medicine: How should researchers report machine learning in...