13E053SOM - Decision Making Systems in Medicine

Course specification
Course title		Decision Making Systems in Medicine
Acronym		13E053SOM
Study programme		Electrical Engineering and Computing
Module		Physical Electronics - Biomedical and Environmental Engineering
Type of study		bachelor academic studies
Lecturer (for classes)		professor PhD Aleksandra Krstić
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		Objective of the course is for the students to be informed about the statistical methods for pattern recognition in terms of medicine: hypothesis testing, parametric and nonparametric classification, clustering.
The outcome		Learning outcomes of the course are following: students´ ability to generate or to collect high quality and informative training sets of data, to apply appropriate statistical pattern recognition technique (hypothesis testing, parametric or nonparametric classifier), to design system for data clustering.
Contents
URL to the subject page		https://automatika.etf.bg.ac.rs/sr/13e053som
URL to lectures		https://teams.microsoft.com/l/team/19%3a17Oac64u07YH57vyVjLTrCgAG9rFGaGPYGMeQ8TLNxw1%40thread.tacv2/conversations?groupId=542562d6-c785-4f46-b04d-922bcdcb349c&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
Contents of lectures		Probability decision-making methods. Evaluation of competing hypotheses Bayesian analysis. Inductive methods based on the minimization of risk. Decision-making methods based on explicit knowledge. Evaluation decision system.
Contents of exercises		Mastering software support for empirical inductive decisions, selecting the most informative attributes in the decision-making process, and evaluating the effectiveness of synthesized systems.
Literature
R.O.Duda, P.E.Hart, "Pattern Classification", Second Edition, John Waley & Sons, 2001. T.Hastie, R. Tibshirani, J. Friedman, "The Elements of Statistical Learning, Data Mining, Inference, and Prediction", Springer, 2001. C.M.Bishop, "Pattern Recognition and Machine Learning", Springer, 2006. R.P.W. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. de Ridder, D.M.J. Tax, PRTools4, A Matlab Toolbox for Pattern Recognition, Delft University of Technology, 2004.
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 and auditory exercises.
Knowledge score (maximum points 100)
Pre obligations		Points	Final exam	Points
Activites during lectures		10	Test paper	30
Practical lessons		30	Oral examination	0
Projects
Colloquia		30
Seminars		0