13M051SOM - Decision Making Systems in Medicine

Course specification
Course title		Decision Making Systems in Medicine
Acronym		13M051SOM
Study programme		Electrical Engineering and Computing
Module
Type of study		master academic studies
Lecturer (for classes)		professor PhD Aleksandra Krstić professor PhD Sanja Vujnović professor PhD Aleksandar Rakić
Lecturer/Associate (for practice)		professor PhD Aleksandra Krstić asst. MA Marija Novičić, electrical and computer engineer
Lecturer/Associate (for OTC)		professor PhD Aleksandra Krstić asst. MA Marija Novičić, electrical and computer engineer
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.
Contents
URL to the subject page		https://automatika.etf.bg.ac.rs/sr/13m051som
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.
Literature
W. Vach, Regression Models as a Tool in Medical Research, CRC Press, 2013. M. Hunink, P. Glasziou, Decision Making in Health and Medicine, Cambridge University Press, 2003. 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. 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