At the end of this course, the student should be able to:
- Understand the fundamental principles of algorithmics and their usefulness for the analysis of biomedical and health data;
- Translate a biomedical or health question into input data, analysis steps, and expected results using algorithmic reasoning;
- Identify and interpret simple algorithms using pseudocode or provided programs.
- Understand how they are used to identify diseases, exposures or health events in different data sources.
- Critically assess their validity and limitations in biomedical research contexts and in real-world health data.
These learning outcomes may be adjusted depending on the schedule, available resources, the number of enrolled students and identified teaching needs. If necessary, they will be specified at the beginning of the course and on the Webcampus platform.
This course aims to familiarize students in biomedical sciences with algorithmic thinking and with translating a scientific problem into a sequence of explicit and reproducible steps.
It should enable them to connect the computer science concepts covered during the practical sessions with concrete applications involving biomedical, clinical and epidemiological data.
Particular attention may be paid to algorithms used to search, classify, transform or summarize health data, as well as to identify diseases, medication exposures and care pathways.
At an introductory level, the course should cover the following topics:
- General principles of algorithmics in bioinformatics;
- Representation, structuring and processing of biomedical data: variables, arrays, functions and files;
- Algorithmic identification of diseases, exposures and health events in health records and healthcare administrative databases;
- Illustrative biomedical applications;
- Biases and limitations of algorithms in health databases and their critical interpretation.
This content may be adjusted depending on the schedule, available resources, the number of enrolled students and identified teaching needs. If necessary, it will be specified at the beginning of the course and on the Webcampus platform.
1. Introduction to algorithmics applied to biomedical and health data
2. Data representation, structuring and processing
3. Algorithms for identifying health events in health databases
4. Illustrative examples
5. Critical appraisal and responsible use of algorithms in health
6. Practical sessions
This table of contents is indicative and may change depending on the schedule, available resources, the number of enrolled students, and identified teaching needs. If necessary, we will specify it at the beginning of the course and on the Webcampus platform.
Teaching combines theoretical courses and practical sessions. Expectations, practical organization and participation arrangements will be specified at the beginning of the course.
Depending on the size and dynamics of the group, activities could include:
- Lectures illustrated with examples from biomedical research, epidemiology and real-world healthcare administrative databases;
- Progressive practical sessions in R/Python covering control structures, arrays, files, tests and algorithmic complexity;
- Short case studies aimed at translating a health question into an algorithm or pseudocode;
- Critical discussions of the validity, performance and limitations of algorithms used in health.
These arrangements may be adjusted depending on the schedule, available resources, the number of enrolled students and identified teaching needs. If necessary, they will be specified at the beginning of the course and on the Webcampus platform.
The learning outcomes should be assessed through a written examination covering the material actually taught and identified during the course as examination material. The examination may include short-answer open questions and/or interpretation of pseudocode (advantages, purpose and limitations) and/or a short application exercise.
Participation in all practical sessions is expected. You may use the concepts and exercises covered in the final assessment, particularly in the form of an algorithm to break down, an algorithm to interpret, and/or a solution to discuss.
The assessment arrangements may be adapted in accordance with institutional rules. If necessary, they will be specified at the beginning of the course and on the Webcampus platform.
Reference books
- Cormen T.H., Leiserson C.E., Rivest R.L., Stein C. Introduction to Algorithms. 4th edition. MIT Press ;
- Goldstein N.D. A Researcher’s Guide to Using Electronic Health Records: From Planning to Presentation. 2nd edition. CRC Press.
Teaching materials
- Course slides made available on Webcampus;
- Programming exercise materials made available on Webcampus;
- Scientific articles, simplified datasets and technical documents selected by the instructor and made available on Webcampus.
| Training | Block | Credits | Mandatory |
|---|---|---|---|
| Master in Biomedical Sciences, Professional focus in Biomedical Data Management | 1 | 8 | Yes |