Artificial Intelligence for Healthcare Data - Summer School
Academic Study Board of the Faculty of Engineering
Teaching language: English
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Summer school (spring)
Course ID: T930017101
ECTS value: 5
Date of Approval: 08-04-2021
Duration: Intensive course
Version: Approved - active
Internal Course Code
Responsible study board
Date of Approval
|Mikkel Baun Kjærgaardemail@example.com||Mærsk Mc-Kinney Møller Instituttet, SDU Software Engineering|
|Sofie Birchfirstname.lastname@example.org||Uddannelsesadministration, Den Tekniske Fakultetsadministration|
|Jan-Matthias Braunemail@example.com||Mærsk Mc-Kinney Møller Instituttet, SDU Applied AI and Data Science|
|Jürgen Herpfirstname.lastname@example.org||Mærsk Mc-Kinney Møller Instituttet, SDU Applied AI and Data Science|
|Manuella Lech Cantuariaemail@example.com||Mærsk Mc-Kinney Møller Instituttet, SDU Applied AI and Data Science|
|Anna Schollainfirstname.lastname@example.org||TEK Uddannelseskoordinering og -support , Den Tekniske Fakultetsadministration|
Completion of minimum 2 years of a related engineering programme, this also includes Computer Science and Data Science
The students should have some rudimentary knowledge in programming and have passed an introductory course in statistics and mathematics (or equivalent).
Learning objectives - Knowledge
I. Describe what role AI can have in medical applications.
II. Explain what medical data can be comprised of, how it can be organized, and why data needs to be treated differently (including storage of sensitive data and rules around the GDPR).
III. Identify which AI tools can be used and describe how the underlying methodology works.
Learning objectives - Skills
Interpret clinical data/health databases.
II. Implement the learned methodology for data preprocessing and AI tools.
III. Present results in a meaningful and concise manner.
Learning objectives - Competences
I. Analyze a problem in the health domain, select, and apply appropriate health data preprocessing, methodology and AI tools to address the problem.
II. Collect and interpret algorithmic output and present a conclusion.
The course will be divided in 4 modules that will bring together technical, modeling, and contextual skills to solve real-life problems:
Module 1 – Introduction to health data
Course introduction (AI and Machine Learning Applied in Health)
How is data collected and stored?
GDPR and ethical considerations
Data cleaning, data preprocessing, data visualization
Module 2 – Using AI for disease diagnosis and early detection
Hands-on practice: “Electrocardiograph markers to predict heart-related complications”
Module 3 – Using AI for patient monitoring based on EEG recordings
Hands-on practice: “Home-monitoring of patients with generalized epilepsy”
Module 4 – Using AI for video feed based medical device development
Hands-on practice: “Use of wireless capsule endoscopy for detecting colorectal cancer”
URL for MySchedule
Combination of lectures, conceptual and programming exercises, and project work. Some of the activities will be conducted in groups.
This course grants 5 ECTS. These are divided between lectures, hands-on classes, assignments’ preparation and exam.
Time of class - Two weeks in august
Number of lessons
hours per semester
Examination is held
At the end of the summerschool course
The examination is based on an overall assessment of:
- Attendance (80%)
- Oral Exam
Form of examination
Second examiner: Internal
7-point grading scale