Course description

Course Contents

Week 1: Medical Information and Knowledge: The nature of medical knowledge, the Information Cycle: Medical Terminology Systems. Week 2-4: 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; guideline-based decision-support systems, unsupervised machine learning and probabilistic medical reasoning and decision-making.

 

Course lay-out

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

 

Educational goals

Knowledge is at the heart of medicine and healthcare processes and comes in various forms: probabilistic definitions of medical terms, relationships between symptoms and diseases, guidelines for treating patients, models for makin rational decisions etcetera. 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 structure and use of terminological systems 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 and obtain skills in decision-support systems and the formalization of medical practice guidelines.
• To understand and apply probabilistic reasoning.

 

Involved departments

• Clinical informatics

 

Evaluation

Evaluation of the course and assignments

The second course of the Master was much more interesting than the first course and was more challenging. I liked the machine learning topic, in a group with two other students we have done our project on this topic. We had to analyze data of the NICE registry and we had to find populations of vulnerable elderly who have a high risk of dying on the Intensive Care Unit and which risk factors are causing this high mortality in the discovered populations. I learned a lot and I improved my writing and communication skills. Furthermore it was very interesting to use some real data and to find some real relations between populations and risk factors. The coordinator of this course was very enthusiastic about the project and helped us a lot. However we had a lot of assignments with deadlines. May be one big assignment and one deadline could do the trick, we had a lot of knowledge about the machine learning part and not about the other two topics. In one assignment it might be possible to combine these three topics.