The Health Data Science group is dedicated to advancing the personalization of care and therapeutics through the design and application of cutting-edge, high-performance machine learning algorithms and methods. Our team integrates interdisciplinary expertise in advanced machine learning, big data analytics, learning-based control theory and biomedical engineering to tackle some of the most challenging topics in personalized medicine. By translating complex electronic medical records, laboratory test results and sensor-based health data into actionable insights and supporting evidence-based decision making, we develop novel, clinically safe, highly scalable predictive models (e.g., federated virtual twins), intelligent clinical decision support systems and closed-loop therapeutical systems. The ultimate objective is to derive and employ reliable, intelligent computational methods essential for delivering medical treatments tailored to individual patients. The impact of our work is reflected through our strong international collaborations with industrial and clinical partners as well as participation in large-scale EU-funded research projects.
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