|Year of selection||2015|
|Institution||The University of Edinburgh|
Type of support
1 500 000 €
A revolution in diabetes’ prediction and prevention, through use of health data
Today about 347 million people worldwide have diabetes, predicted to become the 7th leading cause of death in the world by the year 2030. Increasing the risk of cardiovascular diseases, blindness, and kidney failure, diabetes is a major societal challenge, not only from the health perspective, but also in terms of its economic cost. Transforming risk prediction in diabetes is Prof. Colhoun’s ambition. Thanks to her exceptional expertise in the use of electronic health data to model the risk of diabetes,
she will develop algorithms in order to build and update prediction models for diabetes and risk complications. Prof. Colhoun will benefit from the exceptional environment of the University of Edinburgh, worldwide known for its long tradition of excellence in genetics, a key discipline to understand diabetes.
The University of Edinburgh is also rated amongst the top 5 in the world for computer science, a crucial asset to build effective preventive tools.
The use of personal data being a delicate issue, Prof. Colhoun will work closely not only with experts on machine learning and computer science but also with researchers on data privacy, in order to ensure both data use and protection. Moreover, because prevention needs motivation on the patients’ side, experts on behavioral science will be involved in the program to help identify which measure drive healthy behaviors. Prof. Colhoun strongly believes in the importance of patient-centered interventions to motivate preventive behaviors and avoid useless treatments. That is why, through this ambitious program, she will provide the understanding needed to develop digital tools that help patients make life-style choices. Her research will therefore stimulate advances in predictive and personalized medicine. It will also be crucial to develop collective strategies that could be used beyond diabetes and applied more broadly to other chronic diseases.
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