13M051SOM - Decision Making Systems in Medicine

Course specification
Course title Decision Making Systems in Medicine
Acronym 13M051SOM
Study programme Electrical Engineering and Computing
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 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.
URL to the subject page
Contents of lectures Theoretical basics and application of advanced techniques in the medical domain: Methods for extraction and selection of features. The method of carrier 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 methods for extraction and selection of attributes, implementation of decision-making methods, and the formation of appropriate regression models.
  1. W. Vach, Regression Models as a Tool in Medical Research, CRC Press, 2013.
  2. M. Hunink, P. Glasziou, Decision Making in Health and Medicine, Cambridge University Press, 2003.
  3. Berka, P. (Ed.), Data Mining and Medical Knowledge Management: Cases and Applications, IGI Global, Berka, P. (Ed.). (2009). Data Mining and Medical Knowledge Management: Cases and Applications: Cases and Applications. IGI Global, 2009.
  4. C.M.Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
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 70
Practical lessons 0 Oral examination 0
Projects 30
Colloquia 0
Seminars 0