Data Science Seminar

Digital Medicine and Real-World Machine Learning Applications

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In medical research a common approach is from bench to beside – translate insights gained in research experiments step by step into the clinic, driven by the goal to improve treatment of patients. In an analogous way, we can bring machine learning algorithms to the point-of-care, i.e., integrating them into clinical practice. Where are we in the process of “bits to bedside” and what aspects should we pay attention to?

Biosketch Jens Kleesiek

Jens Kleesiek is full professor at the Institute for Artificial Intelligence in Medicine (IKIM), University Medicine Essen. Focus of his research group lies on the application of unsupervised learning paradigms for recognition of oncologically relevant patterns in large and complex data and the integration of multimodal data sources to enhance the decision-making process at the point of care.
After studying medicine in Heidelberg and bioinformatics in Hamburg, he did his PhD in computer science in 2012. After training at the University Hospital Heidelberg and at the German Cancer Research Center (DKFZ) in Heidelberg he received his specialization in radiology. Prior to his appointment in Essen (2020), he led the computational radiology group at DKFZ, was head of research and development at Gotthardt Healthgroup AG and managing director of the Mediteo GmbH.

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