13M051SOM - Decision Making Systems in Medicine
Course specification | ||||
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Course title | Decision Making Systems in Medicine | |||
Acronym | 13M051SOM | |||
Study programme | Electrical Engineering and Computing | |||
Module | Applied Mathematics, Audio and Video Communications, Audio and Video Technologies, Biomedical and Environmental Engineering, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering, System Engineering and Radio Communications | |||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | none | |||
The goal | The objective of the course is to enable students to master advanced methods for extraction and selection of features, advanced statistical and soft-computing techniques in data mining and decision making in the medical domain and regression models, as a very important tool in modeling medical emergencies. | |||
The outcome | Learning outcome of the course is for students to have the skills to select the most informative attributes from a set of all available attributes, to design advanced decision-making techniques such as Bayes networks and Markov models, and to master methods for modeling the impact of various parameters monitored in medical research. | |||
Contents | ||||
Contents of lectures | Theoretical basics and application of advanced techniques in the medical domain: Methods for extraction and selection of features. The method of support vectors. Bayes Network. Markov's models. Neuro-fuzzy systems. Models of linear and logistical refreshes. | |||
Contents of exercises | Mastering software support for the implementation of advanced methods for extraction and selection of attributes, implementation of advanced decision-making methods, and the formation of appropriate regression models. | |||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
3 | 1 | 1 | ||
Methods of teaching | Lectures (45), auditory exercises (15) and computer exercises (15). | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | 0 | Test paper | 0 | |
Practical lessons | 0 | Oral examination | 40 | |
Projects | 60 | |||
Colloquia | 0 | |||
Seminars | 0 |