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: 13-12-2022
Duration: Intensive course
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 at university level (equivalent to 120 ECTS), this also includes Computer Science and Data Science.
The students should have basic programming experience 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: “Using medical history data to predict prostate cancer metastasis”
Module 3 – Using AI for patient monitoring based on EEG recordings
- Hands-on practice: “Seizure detection for 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”Exercises are performed using standard tools in the corresponding fields, I.e., in R and Python.
URL for Skemaplan
OdenseShow full time table
Combination of lectures, conceptual and programming exercises, and project work. Some of the activities will be conducted in groups.
Number of lessons
supervision hours in total
Examination is held
At the end of the summerschool course
The exam is based on an overall assessment of:
- Individual written report
- Attendance (at least 80 %)
Form of examination
Second examiner: Internal
7-point grading scale
Student Identification Card - Date of birth
The form of examination in the re-examination is the same as in the
ordinary examination except the requirement of 80% attendance which
Enrolment is limited to 25 students. If more applicants than places, applicants who meet the mandatory requirements are prioritised according to the below selection criteria:
1. Undergraduate and graduate students from partner universities (exchange); international undergraduate and graduate guest students (fee-paying); undergraduate and graduate students from other Danish universities.
2. Ph.D students from partner universities and other international Ph.D. students; other applicants.
Students are prioritised on a first-come-first-served basis, i.e. according to the time we receive your complete application.
In case a course is filled up, we try to offer you an alternative course from your list of priorities. All final decisions about admission will be sent out continually.