Univ.-Prof. Dr. med. Fabian Kiessling

Prof. Dr. Fabian Kiessling/ Director Institute for Experimental Molecular Imaging/ Biomedical Big Data Processing, Semester 3

Was Sie in meinem Modul Biomedical Big Data Processing erwartet.

In this module, it will be explained how the different medical data are currently handled and how this will change in the future if big data becomes available for public use (e.g. from databases or smart watches). Next, it will be explained how integrated diagnostics will enable personalized medicine and what the challenges are to bring it into clinical practice. Another focus of the module will be the computational analysis of omics data including radiomics and radiogenomics. In case of the latter, we will explain how quantitative features can be extracted and processed from image data and how computational methods can improve image reconstruction. This will include a short introduction on the most used imaging modalities and microscopic methods.

Warum ist das Modul für Medical Data Scientists wichtig?

Digital, personalized medicine relies heavily on diagnostic data. For the future, it is essential to provide these data quickly and efficiently and to evaluate it comprehensively. In the future, finding the diagnosis and ideal therapy by integrating the versatile diagnostic information will be IT-supported, and research on the concepts and solutions represents a rapidly growing field of research in medicine.

Warum Sie den Master Medical Data Science in Aachen absolvieren sollten!

“Medical Technology and Digital Life Sciense” is the core focus area of the medical faculty in Aachen. Thus, multiple sites strongly investigate novel concepts on big data analysis and integration from a technological and medical angle. Furthermore, several RWTH Centers (such as the Comprehensive Diagnostic Center Aachen, the Center for Telemedicine and the Innovation Center Digital Medicine) aim at realizing these concepts in the clinical environment, thus providing an excellent ground for high level research.