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: 13-12-2022


Duration: Intensive course

Version: Archive

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

Administrative Unit

Mærsk McKinney Møller Instituttet

Date of Approval

13-12-2022

Course Responsible

Name Email Department
Mikkel Baun Kjærgaard mbkj@mmmi.sdu.dk SDU Software Engineering
Sofie Birch sbirch@tek.sdu.dk TEK Uddannelseskoordinering og -support

Teachers

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

Programme Secretary

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

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 at university level (equivalent to 120 ECTS), this also includes Computer Science and Data Science.

Recommended prerequisites

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.

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: “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

Teaching Method

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

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 exam is based on an overall assessment of:

  • Individual written report
  • Attendance (at least 80 %)

Form of examination

Project report

Censorship

Second examiner: Internal

Grading

7-point grading scale

Identification

Student Identification Card - Date of birth

Language

English

ECTS value

5

Additional exam information

The form of examination in the re-examination is the same as in the 
ordinary examination except the requirement of 80% attendance which 
is removed.


Additional information

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.

Courses offered

Offer period Offer type Profile Education Semester

Studieforløb

Profile Education Semester Offer period