Intelligent systems in medicine
- Unit Coordinator: Alexander Schlaefer
- ECTS Credits: 6
- Semester: 1
- Year: 2
- Campus: Hamburg University of Technology
- Language: English
- Aims:
The students are able to analyze and solve clinical treatment planning and decision support problems using methods for search, optimization, and planning.
They are able to explain methods for classification and their respective advantages and disadvantages in clinical contexts.
The students can compare different methods for representing medical knowledge.
They can evaluate methods in the context of clinical data and explain challenges due to the clinical nature of the data and its acquisition and due to privacy and safety requirements.
- Content:
- Methods for search
- Optimization
- Planning
- Classification
- Regression and prediction in a clinical context
- Representation of medical knowledge
- Understanding challenges due to clinical and patient related data and data acquisition
- Students will work in groups to apply the methods introduced during the lecture using problem based learning
- Pre-requisites:
- Principles of math (algebra, analysis/calculus)
- Principles of stochastics
- Principles of programming
- Java/C++ and R/Matlab
- Advanced programming skills
- Reading list:
- Russel & Norvig: Artificial Intelligence: a Modern Approach, 2012
- Berner: Clinical Decision Support Systems: Theory and Practice, 2007
- Greenes: Clinical Decision Support: The Road Ahead, 2007
- Further literature will be given in the lecture
