HPI Digital Health Cluster

Data Management for Digital Health

General Information

  • You are viewing an archived version of the course, please find a newer version here
  • Teaching staff: Aadil Rasheed, Florian Borchert, Dr.-Ing. Mozhgan Bayat, Dr. Matthieu-P. Schapranow
  • Format: 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Schedule:
    • Mondays @ 11.00am (s.t.) on HPI campus II in lecture hall L-E.03.
    • Thursdays @ 11.00am (s.t.) on HPI campus I in lecture hall HS 2.
  • First course:
    • Thu Oct 20, 2022 @ 11.00am (s.t.) on HPI campus I in room: H-2.57/58


  • Mon Feb 13, 2023: Final exams will take place in HPI lecture hall 1.
  • Thu Feb 02, 2023: Join our guest speakers Dr. Sören Lukassen for his talk on "Using Multiple Data Modalities for Brain Tumor Diagnostics and Treatment" (slides) and Matthias Niemann for his talk on "Anonymization of HLA Genotypes for Exchange with Untrusted Parties" (announcement, slides).
  • Thu Jan 26, 2023: Exercise 04 is online available; it is due Feb 02, 2023 end of day (Review slides)
  • Thu Jan 19, 2023: Join our guest speaker Dr. Marcel Naik for his talk on "AI in Nephrology: Leveraging Electronic Health Records for Next-Generation Transplant Care and Research" (announcement, slides).
  • Tue Jan 10, 2023: Exercise 03 is online available; it is due Jan 17, 2023 end of day(Review slides​​​​​​​).
  • Mon Dec 5, 2022: Exercise 02 is online available; it is due Dec 13, 2022 end of day(Review slides​​​​​​​​​​​​​​).
  • Thu Dec 01, 2022: Join our guest speaker Dr. Damian Rieke for his talk on "Precision Oncology: Standards and Challenges for Personalized, Evidence-based and Translational Cancer Therapy in Clinical Routine" (announcement, slides).
  • Mon Nov 21, 2022: Join our guest speaker Prof. Dr. Fabian Prasser for his talk on "Health Data Anonymization in Theory and Practice" (announcementslides).
  • Thu Nov 17, 2022: Build your own DNA workshop will take place in room: L-1.06.
  • Mon Nov 7, 2022: Excercise 01 is online available; it is due Nov 17, 2022 end of day(Review slides).
  • Mon Oct 31, 2022: No lecture due to a bank holiday in Brandenburg state (Reformation Day).
  • Fri Oct 21, 2022: Please note the new rooms assigned for the lecture at the top of the page starting from Monday Oct 24, 2022.
  • Thu Oct 20, 2022: HPI Campus I, room: H-2.57/58 -- due to the high number of interested students, the room for kick off lecture has changed!  
  • Mon Oct 17, 2022: No lecture due to HPI general meeting
  • Thu Sep 22, 2022: Course webpage published.

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).


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%).