Programme

This course will use a problem-based learning approach to provide students with an understanding of recent advances in biomedical engineering, with a focus on the design of clinical decision support systems. Students will be provided with elements of theory associated with the use of the main machine learning techniques, exploring aspects of supervised and unsupervised learning, and exploring the application of these techniques to the general context of health care. Students will be taught to implement these techniques in commonly used programming environments (Python). The students will then work in groups and use the acquired theoretical knowledge and programming skills to solve a project of interest in the field of biomedical engineering, also referring to available databases (MIMIC, Physionet,...). Using the acquired competences in theoretical elements and on the basis of the practical activities performed, students will then be able to concretely validate solutions to real biomedical engineering problems of clinical relevance.

Core Documentation

Material available on the university platform (lecture slides, A/V recordings, guided exercises, project examples)

Type of delivery of the course

Frontal lessons (30%) Guided exercises (30%) Supervised project work (40%)

Attendance

40% of the activities have a project nature, so attendance to the course is highly recommended.

Type of evaluation

development, drafting, presentation and discussion of a project developed around a diagnostic question. The drafting of the project includes the writing of the code needed to solve the diagnostic question.