ENGBMT 75313

At the core of the digitalisation and automation in medicine is the intelligent processing of sensor and other input data. Using and developing artificial intelligence for medical systems requires an understanding of the underlying processes and mechanisms. Technologies are rapidly evolving and more and more medical applications are benefitting from machine learning approaches.  The aim of our seminars is to gain deep knowledge about recent trends in signal processing and applied machine learning to process biomedical data and signals. The participants will study, critically assess, and present recent literature and technological advancements of artificial intelligence (AI) in medicine that drive developments in this exciting research field.

The student learns and practices to

• analyze multidisciplinary literature (MED + ENG + CS)
• stay up-to-date with a particular research topic
• critically assess quality of contributions
• disseminate findings in written and oral form

A list of relevant topics will be provided on moodle at the beginning of the semester. A topic is picked in the first week and the student will elaborate a literature research on this focus topic.

Sample topics

• descision support for sepsis management
• algorithms for automated sleep staging
• federated learning for biosignal processing
• prediction of stroke outcomes
• multimodal deep learning for early detection of neurodegenerative diseases
• single lead ECG estimations
• digital biomarkers
• radar-based biomedical signal processing
• interpretation of wearable data with machine learning
• feature crafting vs deep learning
• transformer networks in medicine

You will analyse the literature and technologies specific to a topic of your choice. During self-guided work you will elaborate a manuscript that covers your literature research.  In a final presentation you will be presenting your results. You will get support by input lectures providing you the basics of scientific work and presentation skills. Peer-review will be also practiced to illustrate processes of scientific dissemination.

These modules are designed for master and doctoral students.