Written exam or oral exam

This lecture comprises different areas of Medical Informatics. The focus is on
data integration, medical data privacy, artificial intelligence and data mining
for health data, and treatment decision support systems. Specific topics are:
• statistical machine learning basics
• state-of-the-art in decision support systems and beyond
• differential privacy
• k-anonymity
• privacy-preserving record linkage
• federated learning approaches and GO-FAIR
• genome privacy
• FHIR
• openEHR
• data warehouses and no-SQL data bases
• map reduce

The students are capable of explaining the most important terms, methods and
theories of clinical decision support systems, medical data privacy, and data
integration and analysis. They are enabled to decide which type of methods fit
to which kind of data sets. The students can critically reflect on shortcomings
of state-of-the-art methods to potentially come up with ideas for extending or
improving the methods.

Eta S. Berner: Clinical Decision Support Systems - Theory and Practice, A.
Gkoulalas-Divanis and G. Loukides: Medical Data Privacy Handbook, P. Lake
and P. Crowther: Concise guide to databases / recommended: Grundlagen der Medizininformatik