MAM02 - Knowledge representation and reasoning in medicine

Knowledge representation and reasoning in medicine

Catalogue number

4604MM002Y

Credits

6

Language of the course

English

Year

1

Time period(s)

Sem. 1 Sem. 2

Educational institute

Medical Informatics

Lecturer(s)

Is part of

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Objectives

Knowledge is at the heart of medicine and health care processes, and comes in various forms: probabilistic definitions of medical terms, relationships between symptoms and diseases, guidelines for treating patients, models for making rational decisions etc. This course equips the student with conceptual tools to understand essential issues in medical informatics concerning the representation and reasoning with knowledge and also provides skills for working with systems that employ these concepts. These essential issues involve the nature of medical knowledge, the anatomy of medical guidelines, the organization of objects in clusters, and the assessment and employment of probabilities in decision making. The objectives of this course are:

  • To get insight in the types and the anatomy of medical knowledge.
  • To understand Terminological Systems.
  • To learn theory in all and obtain skills in one of the following themes;
    • decision-support systems based on clinical practice guidelines
    • advanced machine learning methods
    • probabilistic reasoning in decision making

More Information

Contact information
Prof. dr. A. Abu-Hanna Department of Medical Informatics 
AMC, J1B-113
a.abu-hanna@amc.uva.nl

Contents

The course is organized as follows:

Week 1: Medical Information and Knowledge: The nature of medical knowledge; The Information Cycle; Medical Terminological Systems.

Weeks 2-3-4: The student will get a general introduction in all the following three topics and will, as member of a small group, work for three weeks on a project related to one of the themes:

  • Theme 1: Guideline-based decision-support systems
  • Theme 2: Advanced machine learning methods
  • Theme 3: Probabilistic medical reasoning and decision making

Recommended prior knowledge

Set theory and Logic. 
Artificial Intelligence and Machine Learning.

Format

On Monday of Week 1 the students will get a tutorial on the nature of medical knowledge and on terminology systems and will get an assignment. On Friday of Week 1 a seminar will be held to discuss the assignment. 
During weeks 2 to 4, three tutorials and practicals will be given on the themes 'decision support', 'machine learning', and 'decision making', and the students will work on one of these themes in a project. On the last Friday the students will hand in a report about their project and present their work.

Time

Class times can be found in the course schedule at http://www.rooster.uva.nl.

Study materials

The study materials will be delivered to students.

Min/max participants

The maximum number of participants is 30.

Assessment

The final grade consists for 75% of the attained grade for the final report and for 25% of the attained grade for the presentation on the last Friday. All (other) assignments should be passed.

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