6 ECTS credits
160 h study time
Offer 1 with catalog number 4023340FNR for all students in the 2nd semester at a (F) Master - specialised level.
Position of the course
The purpose of this course is to give students a detailed overview of how data from medical devices, wearables and clinical databases is acquired, stored, processed and visualized, in order to provide insights for clinicians, including medical doctors, nurses and paramedics.
Focus will be on applications that combine software technologies, network technologies, semantic technologies and/or machine learning for usage in hospitals, nursing homes, but also in residential context for healthy living applications.
The students will learn the technologies during the lectures (HOC) and will gain hands-on experience during the specific lab sessions (WPO) using real-life data sets and in project work (ZELF).
Contents
The following topics will be covered during the lectures and will be available in the course notes:
• From medical device/sensor/wearable to data • Health information systems • Network, cloud and software technologies to connect to medical devices, wearables and acquire data from these devices • Data extraction, databases and management of large scale datasets • Healthcare knowledge management Semantics / reasoning / ... • Introduction to machine learning and data mining technologies • Visualization of medical datasets • Data cleaning and preprocessing • Supervised (classification, regression) vs unsupervised (clustering) data mining
Keywords
Data analytics, machine learning, eHealth, big data, medical devices
Initial competences
Basic programming skills in Python, and basic knowledge of algorithms and data structures, signal processing and analysis of systems and signals.
After completion of this course, the student will be able to
• Being familiar with the basic concepts of health information systems and understanding how database systems work • Understand network technologies and protocols tailored to connect medical devices, wearables and databases • Having a comprehensive knowledge about the machine learning process where data 1 is transformed into information and knowledge • Understanding the details of and choice between supervised and unsupervised systems • Interpreting and visualizing the results of a machine learning process or the content of medical datasets • Having a comprehensive knowledge of Python for data analytics purposes • Being able to select, for a given healthcare analytics problem, the most appropriate method to achieve the defined goals
The final grade is composed based on the following categories:
Written Exam determines 70% of the final mark.
Other Exam determines 30% of the final mark.
Within the Written Exam category, the following assignments need to be completed:
Within the Other Exam category, the following assignments need to be completed:
Evaluation methods end-of-term evaluation and continuous assessment
Examination methods in case of periodic evaluation during the first examination period Written examination
Examination methods in case of periodic evaluation during the second examination period Written examination
Examination methods in case of permanent evaluation Skills test, report
Possibilities of retake in case of permanent evaluation examination during the second examination period is possible in modified form
Extra information on the examination methods
Written closed-book exam;
• During semester: graded practicals/lab sessions and project work.
Calculation of the examination mark
• 70% exam • 30% lab sessions/practicals and project
This offer is part of the following study plans:
Master in Applied Sciences and Engineering: Applied Computer Science: Standaard traject
Master of Biomedical Engineering: Startplan
Master of Biomedical Engineering: Profile Radiation Physics
Master of Biomedical Engineering: Profile Biomechanics and Biomaterials
Master of Biomedical Engineering: Profile Sensors and Medical Devices
Master of Biomedical Engineering: Profile Neuro-Engineering
Master of Biomedical Engineering: Standaard traject (NIEUW)