Medical Data Understanding

On the one hand, the demographic trends and the shortage of medical staff (especially in rural areas) critically challenge healthcare systems in industrialised countries. On the other hand, the digitalisation of our society progresses with a tremendous speed, so that more and more health-related data are available in a digital form. For instance, people wear intelligent glasses or/and smart watches, provide digital data with standardised medical devices (e.g., blood pressure and blood sugar meters following the standard ISO/IEEE 11073) or/and deliver personal behavioural data by their smartphones. Pattern recognition algorithms that automatically analyse and interpret that huge amount of heterogeneous data towards prevention (early risk detection), diagnosis, assistance in therapy/aftercare/rehabilitation as well as nursing will experience an extremely high scientific, societal and economic priority in the near future.

One of our core research areas, Medical Data Understanding, addresses the research direction motivated above. It includes the development of original machine learning algorithms for sensor-based holistic health assessment. All steps of modern healthcare systems (prevention, diagnosis, therapy, aftercare, rehabilitation, nursing) become addressed by these investigations. Neurodegenerative diseases experience our highest interest in this context. Two of our externally funded research projects, Cognitive Village: Adaptively Learning, Technical Support System for Elderly (BMBF) and My-AHA: My Active and Healthy Ageing (EC Horizon 2020), deliver resources required for the implementation of this scientific vision and provide interdisciplinary collaboration opportunities, so that, apart from the software development, aspects of hardware, user acceptance as well as ELSI (Ethical, Legal and Social Implications) can also be taken into consideration.