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26M111RBM - Computers in Biomedicine

Course specification
Course title Computers in Biomedicine
Acronym 26M111RBM
Study programme Electrical Engineering and Computing
Module Applied Mathematics, Audio and Video Technologies, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition No
The goal The course aims to provide students with both basic and advanced knowledge of computer technologies in biomedicine, focusing on AI for data analysis, diagnostics, and personalized medicine. It combines theory and practical skills necessary for working with modern biomedical technologies.
The outcome After completing the course, students will understand and apply AI and computer principles in biomedicine, analyze biomedical data, and develop simple diagnostic and prediction models. They will recognize data security, ethical, and legal challenges, and communicate results effectively through documentation and teamwork.
Contents
Contents of lectures Students will learn about hospital information systems, digital health data management, and their role in radiology and genetics. The course covers electronic health records, data standardization, privacy, security (GDPR, HIPAA), and ethical challenges related to AI in medical and genetic data analysis.
Contents of exercises Students will conceptualize and develop a project with an emphasis on the application of artificial intelligence and data visualization. They will be introduced to relevant concepts and tools for machine learning and deep learning, as well as techniques for visualization of biomedical data. The project will be documented, ensuring clarity and transparency of the results.
Literature
  1. Shortliffe, E. H., & Cimino, J. J. (Eds.). (2014). Biomedical Informatics: Computer Applications in Health Care and Biomedicine (4th ed.). Springer
  2. Ramsundar, B., Eastman, P., Walters, P., & Pande, V. (2019). Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More. O’Reilly Media
  3. Bronzino, J. D. (Ed.). (2006). Wiley Encyclopedia of Biomedical Engineering. Wiley-Interscience
  4. Bohr, A., & Memarzadeh, K. (Eds.). (2020). Artificial Intelligence in Healthcare. Academic Press
  5. Yan, K., & Gao, X. (2021). Medical Imaging Informatics. Springer
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
2 2 1
Methods of teaching Oral presentations, presentation of commercial solutions.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 40
Practical lessons 0 Oral examination 0
Projects 40
Colloquia 0
Seminars 20