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13E053SOM - Decision Making Systems in Medicine

Course specification
Course title Decision Making Systems in Medicine
Acronym 13E053SOM
Study programme Electrical Engineering and Computing
Module Biomedical and Environmental Engineering, Physical Electronics - Biomedical and Environmental Engineering, Physical Electronics - Biomedical and Nuclear Engineering, Physical Electronics - Nanoelectronics and Photonics
Type of study bachelor academic studies,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 for students to master basic methods for feature selection and extraction, statistical pattern recognition techniques in the medical domain, neural networks for decision-making in medicine, as well as means of evaluataion of designed decision-making systems.
The outcome Upon completion of the course, students will have skills related to the selection of relevant features and the formulation of high-quality training/testing sets, the design and testing of appropriate decision systems, as well as basic techniques for feature classification and 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%3Al3hvwNFoTadTV0IW6wHFDmBz77KIXOFPSIL6G9rAbrs1%40thread.tacv2/conversations?groupId=ca7944ef-362e-4238-a31e-445d615c7df3&tenantId=1774ef2e-9c62-478a-8d3a-fd2a495547ba
Contents of lectures Random variables and random vectors. Feature extraction and selection methods. Hypothesis testing using Bayesian analysis. Design of linear and quadratic parametric classifiers. Design of nonparametric classifiers. Decision trees. Fundamentals of neural networks.
Contents of exercises Mastering software support (Phyton) for selecting the most informative attributes in the decision-making process, designing decision-making systems, as well as assessing the efficiency of synthesized systems, and in the context of decision-making in medicine by considering relevant databases.
Literature
  1. C.M.Bishop, Pattern Recognition and Machine Learning, Springer, 2006. (Original title)
  2. T.Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction, Springer, 2001. (Original title)
  3. J. Rahman, Brief Guidelines for Mehods and Statistics in Medical Research, Springer, 2015. (Original title)
  4. K. Fukunaga, Introduction to Statistical Pattern Recognition, Prentice Hall, 1992. (Original title)
  5. T. Cleophas, A. Zwinderman, Machine learning in medicine-a complete overview, Cham; Heidelberg: Springer International Publishing, 2015. (Original title)
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
Projects
Colloquia 0
Seminars 0