Computer-Aided Medical Diagnostics
FEKT-LPDGAcad. year: 2017/2018
The course is oriented ot the use of artifficial intelligence in medicine. It is focused on computer-aided medical diagnostics, principles of decision making in medicine, work with uncertainty in medical data, reasoning under uncertainty, principles of fuzzy representation of uncertain information, and structure of expert systems. Students will get experimental knowledgein programming of expert systems.
Learning outcomes of the course unit
The student will be able to:
- describe basic methods of computer processing of biomedical data,
- explain fundamental terms of computer-aided medical diagnostics,
- describe principle of basic methods for probability decision-making,
- discus advantages and disadvantages of the methods,
- design simple expert systems,
- evaluate quality of decision-making methods based on defined requirements.
The student should be able to explain fundamental principles of probability calculus, should know basic terms of data processing and should be oriented in basic knowledge of database systems. Generally, knowledge of mathematics on the level of Bachelor study is required.
Recommended optional programme components
Recommended or required reading
Giarratano, J., Riley, G.: Expert Systems. Principles and Programming. PWS-Publishing Company, Boston, 632 str., 1998. (EN)
Nguyen, H. T., Walker, E. A.: A First Course in Fuzzy Logic. CRC Press, 1997. (EN)
Krishnamoorthy, C. S., Rajeev, S.: Artificial Intelligence and Expert Systems for Engineers. CRC Press, 1996. (EN)
Provazník, I., Kozumplík, J. Expertní systémy. Brno: VUTIUM, 1999. ISBN 8021414863 (CS)
Planned learning activities and teaching methods
Techning methods include lectures and computer laboratories. Course is taking advantage of e-learning (Moodle) system. Students have to write projects/assignments during the course.
Assesment methods and criteria linked to learning outcomes
up to 30 points from computer exercises (individual project)
up to 70 points from finel written exam
The exam is oriented to verification of orientation in terms of computer-aided medical diagnostics and ability to apply basic principles of decision-making in medicine.
Language of instruction
1. Introduction to expert systems, artifficial intelligence.
2. Probability inference in medicine, diagnostic tests.
3. Probability tests, quality of tests, Bayes theorem.
4. Pre-test and post-test probability, sensitivity and specificity, decision trees.
5. Knowledge representation, production rules.
6. Logic in knowledge representation, Venn diagrams, propositional logic.
7. Inference, modus ponens.
8. Proof of claim, resolution rule.
9. Examples of resolution.
10. Uncertainty and uncertain inference.
11. Fuzzy sets.
12. Fuzzy logic.
The aim of the course is to inform students about principles of computer-aided diagnostics in medicine using artifficial intelligence and design of simple diagnostics systems used in medicine.
Specification of controlled education, way of implementation and compensation for absences
Computer exercises are obligatory. Excused absence can be substituted.
Classification of course in study plans
- Programme EEKR-ML1 Master's
branch ML1-BEI , 2. year of study, winter semester, 4 credits, optional specialized
- Programme EEKR-ML Master's
branch ML-BEI , 2. year of study, winter semester, 4 credits, optional specialized
- Programme EEKR-CZV lifelong learning
branch ET-CZV , 1. year of study, winter semester, 4 credits, optional specialized