Digital health data promises to further our understanding of an individual’s disease risk and to discover determinants of differential outcomes. Such analyses are experiencing a boost in different countries by aggregating anonymised medical records of large parts of the population in secure trusted research environments. The Danish healthcare system is one of the leading countries for systematic electronic healthcare information.
Additionally, different cohorts, such as the UK Biobank, exist that have genomically profiled hundreds of thousands of individuals and characterised their health via diverse assays from MRI-scans to detailed blood counts in conjunction with linking to their medical records. Yet analysing these troves of data from millions of individuals easily overwhelms most research applications.
As part of this international NovoNordisk funded research project, we aim to analyse the electronic health records of 8 million Danes to model and quantify the influence of medical history, family history, genetics and lifestyle on future disease risk. We have developed machine learning methods to scale such analyses to data from millions of participants and quantify the influence of thousands of different past and current events. We plan to conduct these analysis across the entire catalogue of ICD-10 disease codes.
The resulting detailed risk assessments will be of unprecedented depth for each individual, will enable the identification of new risk factors and ultimately allow better schemes for disease prevention to be developed. Such analyses will also be replicated in the UK using resources provided by the UK Biobank and NHS digital. The work will be conducted in the research groups Ewan Birney (EMBL-EBI, Cambridge UK) and Moritz Gerstung (DKFZ, Heidelberg, Germany)
Closing date: 15 June 2021
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