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: Approved - active

Course ID

T930017101

Course Title

Artificial Intelligence for Healthcare Data - Summer School

ECTS value

5

Internal Course Code

XSSB-AIHD

Responsible study board

Academic Study Board of the Faculty of Engineering

Date of Approval

08-04-2021

Course Responsible

Name Email Department
Mikkel Baun Kjærgaard mbkj@mmmi.sdu.dk Mærsk Mc-Kinney Møller Instituttet, SDU Software Engineering
Sofie Birch sbirch@tek.sdu.dk Uddannelsesadministration, Den Tekniske Fakultetsadministration

Teachers

Name Email Department City
Jan-Matthias Braun j-mb@mmmi.sdu.dk Mærsk Mc-Kinney Møller Instituttet, SDU Applied AI and Data Science
Jürgen Herp herp@mmmi.sdu.dk Mærsk Mc-Kinney Møller Instituttet, SDU Applied AI and Data Science
Manuella Lech Cantuaria mlca@mmmi.sdu.dk Mærsk Mc-Kinney Møller Instituttet, SDU Applied AI and Data Science

Programme Secretary

Name Email Department City
Anna Schollain avs@tek.sdu.dk TEK Uddannelseskoordinering og -support , Den Tekniske Fakultetsadministration

Offered in

Odense

Level

Bachelor

Offered in

Summer school (spring)

Duration

Intensive course

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 MySchedule

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

English

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 exam

Censorship

Second examiner: Internal

Grading

7-point grading scale

Language

English

ECTS value

5

Courses offered

Period Offer type Profile Programme Semester

Studieforløb

Profile Programme Semester Period