Module CS4368-KP06
Advanced Data Analysis Methods for Digital Health Applications (ADA)
Duration
1 Semester
Turnus of offer
irregularly in the winter semester
Credit points
6
Course of studies, specific fields and terms:
- Master Medical Informatics 2019, optional subject, Medical Data Science / Artificial Intelligence
Classes and lectures:
- Advanced Data Analysis Methods for Digital Health Applications (exercise, 2 SWS)
- Advanced Data Analysis Methods for Digital Health Applications (lecture, 2 SWS)
Workload:
- 60 hours work on project
- 60 hours in-classroom work
- 20 hours exam preparation
- 40 hours private studies
Contents of teaching:
- Process of Relevant Physiological Biomedical Signals
- Acquisition of Biomedical Data (Sensors and sources of measurement errors)
- Signal Processing of Biomedical Signals
- Machine Learning Approaches for Biomedical Data
- Data Analysis Methods (Statistical, explainability)
- Student Project including Result-Presentation
Qualification-goals/Competencies:
- Students can explain the mechanisms of signal acquisition in relation to physiological functioning and propose suitable modalities for signal acquisition.
- Students can specify and explain the interaction between physiological functioning/phenomena, specific signal variations, and functional, neurological, and cardiovascular diseases.
- Students can select and setup appropriate measurement modalities, experimental setups for signal acquisition, as well as signal processing and machine learning approaches for specific physiological phenomena and diseases.
- Students can review and assess the data-quality in terms of potential errors and signal-to-noise ratio, and interpret the results in relation to specific medical questions.
- Students can illustrate and discuss their concepts, solutions, and results.
- Students can design and propose new studies for analyzing physiological signals.
Grading through:
- portfolio exam
Responsible for this module:
Language:
- English, except in case of only German-speaking participants
Notes:
Admission requirements for taking the module:- None (the competences of the modules mentioned under ''requires'' are needed for this module, but are not a formal prerequisite).
Admission requirements for participation in module examination(s):
- Successful completion of exercise slips as specified at the beginning of the semester.
Module Exam(s):
- CS4368-L1: Advanced Data Analysis Methods for Digital Health Applications, portfolio exam consisting of: 60% for 90-minute written or oral examination (at the discretion of the lecturer) and 40% for an independent project work.
Last Updated:
28.11.2025