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Region Hovedstaden - en del af Københavns Universitetshospital
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Diabetes and related complications as well as Alzheimer's Disease.

Primære forskningsområder

Use machine learning and statistics to understand and predict disease. Classical statistics tools are used to extract predictive power and medical understanding from patient data. Machinelearning tools such as random forests and neural network algorithms are used to classify disease states and regression algorithms like lasso and linear regression are used to predict values of disease related features. Resulting models might be used to improve diagnosis and disease treatment or for finding important associations between measured features and disease. 

Aktuel forskning

- Diabetes complications (the PROTON study)

- Early Alzheimer's disease biomarker discovery (the EMIF-AD project)

ID: 53714803