Artificial Intelligence for Healthcare Data - Summer School
Academic Study Board of the Faculty of Engineering
Teaching language: English
EKA: T930017102
Censorship: Second examiner: Internal
Grading: 7-point grading scale
Offered in: Odense
Offered in: Summer school (spring)
Level: Bachelor
Course ID: T930017101
ECTS value: 5
Date of Approval: 08-04-2021
Duration: Intensive course
Version: Archive
Course ID
Course Title
ECTS value
5
Internal Course Code
Responsible study board
Administrative Unit
Date of Approval
Course Responsible
| Name | Department | |
|---|---|---|
| Mikkel Baun Kjærgaard | mbkj@mmmi.sdu.dk | SDU Software Engineering, Mærsk Mc-Kinney Møller Instituttet |
| Sofie Birch | sbirch@tek.sdu.dk | TEK Uddannelse, Det Tekniske Fakultet |
Teachers
| Name | Department | City | |
|---|---|---|---|
| Jan-Matthias Braun | j-mb@mmmi.sdu.dk | Applied AI and Data Science, Mærsk Mc-Kinney Møller Instituttet | |
| Jürgen Herp | herp@mmmi.sdu.dk | Applied AI and Data Science, Mærsk Mc-Kinney Møller Instituttet | |
| Manuella Lech Cantuaria | mlca@mmmi.sdu.dk | Applied AI and Data Science, Mærsk Mc-Kinney Møller Instituttet |
Programme Secretary
| Name | Department | City | |
|---|---|---|---|
| Anna Schollain | avs@tek.sdu.dk | TEK Uddannelseskoordinering og -support, Det Tekniske Fakultet |
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Completion of minimum 2 years of a related engineering programme, this also includes Computer Science and Data Science
Recommended prerequisites
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.
Content
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 Skemaplan
Teaching Method
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
Teaching language
Examination regulations
Exam regulations
Name
Exam regulations
Examination is held
At the end of the summerschool course
Tests
Exam
EKA
T930017102
Name
Exam
Description
The examination is based on an overall assessment of:
- Attendance (80%)
- Oral Exam
Form of examination
Oral examination
Censorship
Second examiner: Internal
Grading
7-point grading scale
Language
English
ECTS value
5