Clinical Decision Support

Course content

Rule-based knowledge, decision trees (Classification and Regression Trees), Arden syntax, fuzzy logic

Learning outcomes

The aim of the focus is to understand and apply AI-supported methods for data analysis from the domain of health. This includes methods of machine learning, fuzzy logic, decision support, big data analysis, etc. Building on the knowledge gained in the Bachelor's program, students learn statistical and mathematical methods specifically required for this purpose.

Recommended or required reading and other learning resources / tools

Books: Jähn/Nagel: eHealth, Mitchell, T.M. (1997): Machine Learning. McGraw-Hill.
Haykin, S. (1999): Neural Networks, A Comprehensive Foundation, 2nd Edition. New Jersey: Prentice-Hall.
Russel, S. and Norvig, P. (2004): Artificial Intelligence – A Modern Approach. New Jersey: Prentice-Hall.
Russel, S. and Norvig, P. (2004): Künstliche Intelligenz - Ein moderner Ansatz. 2. Auflage, München: Pearson Studium.
Sutton, R. and Barto, A. (1998): Reinforcement Learning. Cambridge, London: MIT Press.
Engelbrecht, A.P. (2007): Computational Intelligence - An Introduction. Wiley & Sons.
Kramer, O. (2009): Computational intelligence. Berlin: Springer.
Bishop, C.M. (2005): Neural Networks for Pattern Recognition. New York: Oxford University Press.Witten, I.H. and Frank, E. (2005): Data Minig. Practical Machine Learning Tools and Techniques. 2nd edition, San Francisco: Elsevier. Poole, D., Mackworth, A. and Goebel, R. (1998): Computation Intelligence - A Logical Approach. New York: Oxford University Press.Beierle, C. and Kern-Isberner, G. (2008): Methoden wissensbasierter Systeme. 4. Auflage, Wiesbaden: Vieweg & Teubner. Spreckelsen, C. and Spitzer, C. (2008): Wissensbasen und Expertensysteme in der Medizin. Wiesbaden: Vieweg & Teubner.Wiltgen, Marco (1999): Digitale Bildverarbeitung in der Medizin. Aachen: Shaker.

Mode of delivery

5 ECTS ILV

Prerequisites and co-requisites

Mathematics, Statistics, Software-Developement, Medical Standards

Assessment methods and criteria

Lecture: Exam; Tutorial: Continuous Assessment