Big Data Medicine – University of Copenhagen

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Health > About The Faculty > Impact in Society > Translational Research > Big Data Medicine

A data-driven revolution in treatment 

 Servers with big data

Researchers at the University of Copenhagen target their work using health data. This can improve diagnosis and treatment success, reduce side effects and increase researchers’ and health professionals’ knowledge of both common and more complex diseases.


Big Data has the potential to increase productivity in the life science sector by 0.7 percent per year.

Some of the most comprehensive health databases in the world have been developed in Denmark. These are run and studied by leading international specialists. For this reason, the Danish National Health service has particularly good conditions for making use of this health data in research projects to prevent disease and improve the treatment of patients. 


Denmark is ranked No. 1 for the implementation of e-health.

The faculty has entered into a strong cooperation with hospitals and other health care professionals to collaborate on research and treatment using Big Data.

Big Data Medicine compiles information about patients' health, previous diagnoses, hospitalizations, scans, x-rays, blood pressure measurements, blood tests and heart examinations. 

The data that is collected can, for instance, give an overview of the patient’s medication intake, and lifestyle in terms of diet, exercise and alcohol use.


The public health service data administration’s National Patient Register has been collecting the health data of Danes since 1995.

By analysing the health data, researchers and physicians can achieve a 360° overview of a patient’s medical records, and draw on the experience that has been gained from similar diagnoses and treatments in other patients. 

"By comparing data from hundreds of thousands of patients, we can effectively support research into the prevention and treatment of diseases. This can save individuals from receiving unnecessary and ineffective treatment, and it greatly reduces costs for the health service", says Professor Søren Brunak from the Novo Nordisk Foundation Centre for Protein Research at SUND.

By studying data patterns, it is possible to predict how a particular type of treatment will affect a patient. It can also prevent medical errors and reduce side effects.  

Data patterns can prevent disease

Rigshospitalet, the Technical University of Denmark, the Health Data Protection Agency and the University of Copenhagen are all working closely together on a number of research projects whose purpose is to find new patterns and trends in patient care. 

At SUND’s Centre for Protein research, the data from thousands of blood tests are used to examine how proteins in the blood can supply evidence of the increase in lifestyle diseases such as type 2 diabetes, obesity and other metabolic diseases which may have major implications for society. The results will be used to initiate early preventative treatments.

"Big Data is an extremely exciting factor in protein research.  At the moment, we are the only ones in the world that compare measurements of proteins in the blood with data from other disease patterns to see if a person is at risk of developing diabetes. The technology could prove to be a revolution in global healthcare", says Professor Matthias Mann from the Novo Nordisk Foundation Centre for Protein Research.

Big Data increases the chances of survival for intensive care patients

The Faculty and The Department of Intensive Care at Rigshospitalet are working together on a study to identify a new measurement model for intensive care patients’ survival.

When patients with acute, life-threatening illnesses receive treatment at the country’s intensive care units, doctors have great difficulties predicting the patients’ chances of survival. The prognosis is typically made in reaction to a patient’s first 24 hours after admission. But by using Big Data, doctors can include the previous experiences of the past twenty years in their assessment of a patient’s chances of survival.

"We would like to be much better at predicting the outcome of a patient’s  recovery. Therefore, we cooperate with The University of Copenhagen to develop models that can predict our patient’s chances of survival and the prognosis for recovery – also after they have been discharged from the hospital. Big Data can provide us with many answers", says Professor and Consultant Anders Perner from The Centre for Research in Intensive Care at Rigshospitalet.