Welcome to the class: We are very excited that you are interested in learning more about the priciples of data management for digital health and why it might be different from what you have learnt so far. In this lecture, you will learn about:
- Specific examples from selected fields of digital health to understand where and how data is/needs to be acquired,
- Known challenges in acquiring and processing these types of data in their specific digital health domain,
- How to deal with and address specific requirements and limitation of accessing and using digital health data, and
- How the complex analysis of high-dimensional multi-modal digital health data can be facilitated through the use of latest soft- and hardware advances, e.g. clinical prediction models, federated learning infrastructures, large language models, and supervised as well as unsupervised machine learning approaches.
After participating in the course, you will be equipped with the ability to:
- Assess requirements of selected real-world use cases from the medical field (oncology, nephrology, infectious diseases),
- Select latest technology building blocks to create viable healthcare software solutions, and
- Analyze requirements for data analysis and processing, e.g. for machine learning (supervised and unsupervised learning, large language models and natural language processing, deep learning, federated learning infrastructures).
In the course, invited guest speakers will join us and share their real-world experience with you in interactive presentations. As a result, you will have the chance to raise any questions you never dared to ask and discuss them together with us in the course of the lecture.
Further details about the structure of the lecture will be shared with you in the kickoff lecture (please check the date on the top of this page).
