HPI Digital Health Cluster

Hands-on Artificial Intelligence for Digital Health

General Information

  • Teaching staff: Florian Borchert, Dr. Matthieu-P. Schapranow
  • Course kickoff: Thu Apr 18, 2024 @ 11.00am s.t.
  • Dates & times (selected dates for joint presentations only):
    • Thu 11.00am-12.30 pm s.t. (Campus II, L-1.06)
  • 4 Semesterwochenstunden (SWS) 6 ECTS (graded)
  • Limit: Max. number of participants defined by the number of provided topics.
  • After the kickoff event in the first course, you have to send us your preferred seminar topics (due date will be mentioned in the kickoff slides). Afterwards, you will be assigned to one of your preferred topics, which needs to be confirmed through official course enrollment by you.



  • Topics and selection procedure will be presented during the kickoff event.



  • A: LLM-based Named Entity Recognition for Genes and Variants
  • B: BioASQ MultiCardioNER (Shared Task)
  • C: Entity Normalization for Tumor Morphologies
  • D: Gene Name Normalization
  • E: Explore ML applications for *omics data
  • F: Can ML support discovery new cancer subtypes?
Photo by AltumCode

Scope of the seminar


The purpose of this seminar is to acquire hands-on experience on applying latest methods from Machine Learning (ML) and Artificial Intelligence (AI) to selected real-world use cases from Digital Health (DH). You will select your project preference from a list of available seminar topics presented in the kick-off event. The assigned project topic defines the focus of your individual contribution throughout the remainder of the seminar. You will broaden your ML/AI skills on a real-world DH use case, apply selected ML/AI methods, and evaluate and interprete your obtained results.

Your tutors will introduce selected ML/AI technologies and tools to you, which are relevant for your chosen seminar projects. You will acquire hands-on experience with these tools and apply them to your chosen real-world scenarios and realistic data sets. Please bear in mind: Some of the used data sets might require you to either sign-up on a webpage, agree to follow data handling steps, sign a data use or confidentially agreement, or similar aspects. We will equip you with the required ML/AI expertise and provide you access to additional materials for your chosen projects.

What we can expect from you

We expect you to:

  • Deep-dive into the required ML/AI technology,
  • Conduct research on related work in the specific field,
  • Design and apply your own ML/AI approach in the context of your seminar topic, and
  • Evaluate your approach and compare it to results from related work. 

Please assess yourself: You should have profound programming/development skills and build on exisiting ML/AI foundations prior to join the seminar. 

What you can expect from us

You can expect from us a continous coaching with regards to your select project topic throughout the whole semester, e.g. in regular appointments with your tutor. Furthermore, we will provide you guidance for improving your presentation and scientific writing skills. You will share your individual results in an intermediate and a final presentation with all participants. The presentation will help you to communicate your approach and intermediate results to others as well as to receive individual feedback on your approach and individual progress. Ultimately, you will document your findings in a scientific report at the end of the seminar.


The final grading will be determined by the following individual parts, each of them must be passed individually: 

  • Seminar results, i.e. intermediate + final presentation conducted during the seminar slots as well as research prototype (40%), 
  • Research article about your individual contribution submitted at the end of the seminar (40%), and
  • Individual commitment throughout the seminar (20%).