Hasso-Plattner-Institut
HPI Digital Health Center
 

Data Management for Digital Health

General Information

News

  • Thu Sep 22, 2022: Course webpage published.
  • Mon Oct 31, 2022: No lecture due to a bank holiday in Brandenburg state (Reformation Day).

Slides

Slides will be published here shortly after the corresponding lecture.

Scope of the lecture

Welcome to the class: We are very excited that you are interested in learning more about the foundations of data management for digital health. In this lecture, we will provide you specific examples from the field of digital health to understand where and how data is acquired, which challenges are specific for these types of data and how to address them, and how to benefit from the analysis of high-dimensional digital health data with latest technology advances, such as machine learning.

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, natural language processing, image analysis).

In the course, we will have invited guest speakers sharing their real-world experience with you in interactive presentations. Thus, 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).

Grading

In the course of the lecture, you will have to conduct a small number of personal exercises to recap the presented lecture content. You have to pass all of these intermediate exercises prior to participate the final exam (Prüfungsvorleistung).

The final grading will be determined by the following parts:

  • Final exam at the end of the course (100%).