Course detail

Computer-Aided Medical Diagnostics

FEKT-MPDGAcad. year: 2010/2011

The use of artifficial intelligence in medicine. Computer-aided medical diagnostics (CAMD), its applications, design of CAMD systems, meaning and the use of knowledge. Principles of decision making in medicine, medical data, interpretation of diagnoses. Uncertainty in medical data, reasoning under uncertainty. Principles of fuzzy representation of uncertain information. Fuzzy logic for CAMD. Structure of expert systems, meaning of knowledge and facts, inference. Representation of medical knowledge. Programming of expert systems. Knowledge engineering, cooperation of a knowledge engineer and a medical expert.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Ing. Valentine Provazník, Ph.D.

Department

Department of Biomedical Engineering (UBMI)

Learning outcomes of the course unit

Advanced knowledge in computer-aided medical diagnostics with particular emphasis to expert systems.

Prerequisites

The subject knowledge on the Bachelor´s degree level is requested.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The course should provide the student with the basic theoretical principles of computer-aided diagnostics in medicine using artifficial intelligence. Design of simple diagnostics systems used in medicine including their relation to therapeutic planning systems.

Specification of controlled education, way of implementation and compensation for absences

The content and forms of instruction in the evaluated course are specified by a regulation issued by the lecturer responsible for the course and updated for every academic year.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Giarratano, J., Riley, G.: Expert Systems. Principles and Programming. PWS-Publishing Company, Boston, 632 str., 1998. (EN)
Krishnamoorthy, C. S., Rajeev, S.: Artificial Intelligence and Expert Systems for Engineers. CRC Press, 1996. (EN)
Nguyen, H. T., Walker, E. A.: A First Course in Fuzzy Logic. CRC Press, 1997. (EN)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme EEKR-M Master's

    branch M-BEI , 2 year of study, winter semester, elective specialised

  • Programme EEKR-CZV lifelong learning

    branch EE-FLE , 1 year of study, winter semester, elective specialised

Type of course unit

 

Lecture

39 hod., optionally

Teacher / Lecturer

prof. Ing. Valentine Provazník, Ph.D.

Syllabus

Introduction to application of artifficial intelligence (AI) in medicine. Computer-aided medical diagnostics (CAMD), its applications, programming languages of AI, design of CAMD systems, expert systems, meaning and the use of knowledge.
Principles of decision making in medicine, medical data, information, knowledge , metaknowledge, hypotheses, statistics in decision making, diagnosis intrepretation.
Uncertainty in medical data, reasoning under uncertainty, traditional Bayesian probability v. factors of uncertainty in medicine.
Measure of belief and disbelief in inference, similarity with human reasoning, principles of fuzzy representation of uncertain information.
Fuzzy numbers, fuzzy relations and fuzzy logic for CAMD.
Structure of expert systems, meaning of knowledge and facts, inference.
Representation of medical knowledge, production rules, decision trees.
Deductive logic and predicate logic in medical diagnostics.
Logic systems and resolution methods, forward and backward chaining of knowledge.
Programming of expert systems, fundamentals of CLIPS language, examples of design of expert systems in CLIPS.
Knowledge engineering, cooperation of a knowledge engineer and a medical expert in knowledge mining, priciples od expert system design.
Fuzzy rules in expert systems.
Inference composition rule in medical expert systems, defuzzification for diagnosis.

Exercise in computer lab

13 hod., optionally

Teacher / Lecturer

Ing. Jiří Sekora, MBA

Syllabus

Individually solved projects of design of expert system as systems of computer-aided medical diagnostics.