CourseSpecifications
Valid as from the academic year 2024-2025

Data Analytics in Healthcare and Connected Care (E027770)

Course size
Credits 6.0(nominal values; actual values may depend on programme)
Study time 180 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

B (semester 2)

Dutch

practicallecture

Gent

Lecturers in academic year 2024-2025
Ongenae, Femke
Van Hoecke, Sofie
De Turck, Filip

TW05
TW06
TW05

Offered in the following programmes in 2024-2025
Master of Science in Biomedical Engineering
Master of Science in Biomedical Engineeringstaff member
lecturer-in-charge
co-lecturer
crdts
66offering
B
A

Teaching languages
English, Dutch
Keywords
Data analytics, machine learning, eHealth, big data, medical devices
Position of the course
The purpose of this course is to give students a detailed overview of how data from medicaldevices, wearables and databases can be acquired, processed and visualized, in order toprovide insights for medical doctors, nurses and paramedics. Focus will be on distributed
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 and will gain hands-on experienceduring the specific lab sessions using real-life data sets.
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:• • Network, cloud and software technologies to connect to medical devices, wearables and• • acquire data from these devices• Data extraction, databases and management of datasets• • From SQL to NoSQL• Healthcare knowledge management• • Semantics / reasoning / ...
• Introduction to machine learning and data mining technologies• • Visualization of medical datasets• • Data cleaning• • Supervised (classification, regression) vs unsupervised (clustering) data mining• • Working with different types of data (time series, text, images, video)• • Working with different machine learning techniques (basic and state-of-the-art techniques)
Initial competences(Approved)

1Basic programming skills in Python, and basic knowledge of algorithms and data structures (e.
g. as acquired in the bachelor courses 'Discrete Mathematics' and 'Informatics').
Core courses from the Biomedical Engineering program, such as Signal Processing andAnalysis of Systems and Signals.
Final competences
1 Understand network technologies and protocols tailored to connect medical devices,
1 wearables and databases
2 Being familiar with the basic concepts of database systems and databases and
1 understanding how database systems work
3 Having a comprehensive knowledge about the machine learning process where data is
1 transformed into information and knowledge
4 Understanding the details of and choice between supervised and unsupervised systems
5 Interpreting and visualizing the results of a machine learning process or the content of
1 medical datasets
6 Having a comprehensive knowledge of Python for data analytics purposes
7 Being able to select, for a given healthcare analytics problem, the most appropriate method
1 to achieve the defined goals
8 Being able to construct datasets by querying APIs
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Lecture, Practical
Study material
Type: Slides
Name: Slides on Ufora
Indicative price: Free or paid by faculty
Optional: noLanguage : English
Number of Slides : 2000
Oldest Usable Edition : Yearly update of slides
Available on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
References
References to relevant hand books will be provided to the students
Course content-related study coaching• Lab sessions are supervised by teaching assistants• Additional information available via Ufora
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment
Examination methods in case of periodic assessment during the second examination period
Written assessment
Examination methods in case of permanent assessment
Skills test, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible in modified form
Extra information on the examination methods• During examination period: written closed-book exam; written open-book exam.
• During semester: graded practicals/lab sessions, participation to each lab is required.
Calculation of the examination mark(Approved)2• 75% exam
• 25% lab sessions/practicals
In order to pass the course, the student must obtain at least 8/20 for the PE (exam) and theNPE (handing in all lab session and lab practical reports, with a minimum average score of8/20). If this condition is not met, the final score will deviate from the calculated score if 10 ormore was obtained and the student will receive a score of 9/20.

(Approved)3