13E053SOM - Decision Making Systems in Medicine

Course specification
Course title Decision Making Systems in Medicine
Acronym 13E053SOM
Study programme Electrical Engineering and Computing
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
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, as well as decision-making techniques based on neural networks and the fuzzy logic.
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, to use neural networks in decision making process, to construct a fuzzy expert system.
URL to the subject page
URL to lectures
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. Neural networks. Fuzzy logic. Evaluation of decision system.
Contents of exercises Mastering software support for empirical inductive decision making, selecting the most informative attributes in the decision-making process, and evaluating the effectiveness of synthesized systems, as well as the selection and implementation of an appropriate classification method, whether it's statistical or soft-computing techniques.
  1. K. Fukunaga, Introduction to Statistical Pattern Recognition, Prentice Hall, 1992.
  2. C.M.Bishop, "Pattern Recognition and Machine Learning", Springer, 2006.
  3. S. Barro, R. Marin, Fuzzy Logic in Medicine, Springer-Verlag Berlin Heidelberg, 2002
  4. R. Dybowski, V. Gant, Clinical applications of artificial neural networks, Cambridge University Press, 2001.
  5. J. Rahman, Brief Guidelines for Mehods and Statistics in Medical Research, Springer, 2015.
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 30 Oral examination 0
Colloquia 0
Seminars 0