Health Information and Decision Support

3 ECTS credits
90 h study time

Offer 1 with catalog number 4021345FNR for all students in the 2nd semester at a (F) Master - specialised level.

Semester

2nd semester

Enrollment based on exam contract

Impossible

Grading method

Grading (scale from 0 to 20)

Can retake in second session

Yes

Enrollment Requirements

Students must have taken ‘Data Analytics in health and connected care', before they can enroll in ‘Health Information and Decision Support’

Taught in

English

Partnership Agreement

Under interuniversity agreement for degree program

Faculty

Faculteit Ingenieurswetenschappen

Department

Electronics and Informatics

Educational team

Jef Vandemeulebroucke (course titular)

Activities and contact hours

18 contact hours Lecture
18 contact hours Seminar, Exercises or Practicals

Course Content

Goal of the course

The goal of this course is to provide an overview of the field of clinical decision support. The role of decision support in clinical care is presented. Different types of approaches are explained, based on the type of data on which they operate, and the types of techniques which they employ.

A brief introduction to health information systems is given. Knowledge-based systems for decision support are introduced, including rule-based systems, fuzzy logic systems and Bayesian belief networks. Next, data-driven approaches are discussed, and several popular methods from machine learning are introduced. Specific attention is given to computer-aided diagnosis, for providing decision support for unstructured data such as biomedical signals and images. Methods for improving learning from data are desribed. Finally, considerations for system design, validation, certification and ethics are discussed.

Contents

Introduction to health information systems
Knowledge-based decision support systems: rule-based systems, fuzzy logic systems and Bayesian belief networks
Data-driven decision support systems: perceptron, support vector machine, decision trees, nearest neighbor, neural networks
Learning aides: data balancing, normalization, feature selection, ensemble methods, bagging and boosting
Computer-aided diagnosis: feature extraction and classification, convolutional neural networks
System design, validation, certification and ethical considerations

Practical sessions and exercises:

The lectures are supported by 4 practical sessions, covering selected topics from the lectures:

Introduction to data loading, processing and visualization
Knowledge-based decision support systems
Data-driven decision support systems
Computer-aided diagnosis

Course material

Digital course material (Required) : Slides presented during lectures
Digital course material (Required) : Exercises from practical sessions
Digital course material (Required) : Provided scientific articles

Additional info

Lectures will cover the theoretical part of the course. Practical sessions will consist of exercises in which the concepts seen during the lectures are applied. Practical sessions will be guided by assistants. Reports on the practical sessions can be finalized afterwards.

Learning Outcomes

Final competences

After completing this course, the student will be able to:

List the current information systems used in health care, differentiate the type of information that is stored, and identify the current limitations
Illustrate the principle of knowledge-based decision support systems, explain the techniques treated during the lectures, and apply such decision support given the description of real medical problems
Illustrate the principle of data-driven decision support systems, explain the techniques treated during the lectures, and apply such decision support given real data sets
Summarize the main problems which can occur when learning from data and carry out common operations that can aid the learning process.
Illustrate the principle of computer-aided diagnosis, explain the techniques treated during the lectures, and apply such algorithms on real signals and images
Compare the different types of systems and identify the advantages and disadvantages
Outline the current possibilities and opportunities for clinical decision support systems, and the technological and legal challenges.

MA_A: KNOWLEDGE ORIENTED COMPETENCES

2. integrated structural design methods in the framework of a global design strategy
5. conceive, plan and execute a research project, based on an analysis of its objectives, existing knowledge and the relevant literature, with attention to innovation and valorization in industry and society
7. present and defend results in a scientifically sound way, using contemporary communication tools, for a national as well as for an international professional or lay audience

MA_B: ATTITUDE

12. a creative, problem-solving, result-driven and evidence-based attitude, aiming at innovation and applicability in industry and society
14. consciousness of the ethical, social, environmental and economic context of his/her work and strives for sustainable solutions to engineering problems including safety and quality assurance aspects

MA_C: SPECIFIC BIOMEDICAL KNOWLEDGE

18. To apply acquired knowledge and skills for the design, development, implementation and evaluation of biomedical products, systems and techniques in the health care sector.
20. To take a leading role in a multidisciplinary team to ensure the quality and compliance with regulations and standardization of medical equipment and medical procedures, and to communicate with stakeholders.
21. To be aware of the ethical and socio-economic boundary conditions of research, and act professionally within the context of biomedical technology.

Grading

The final grade is composed based on the following categories:
Oral Exam determines 65% of the final mark.
Practical Exam determines 35% of the final mark.

Within the Oral Exam category, the following assignments need to be completed:

Oral exam theory with a relative weight of 1 which comprises 65% of the final mark.

Note: Oral exam with written preparation (closed book). After the questions are given, students will be given time to prepare the answers on paper, before explaining these during oral examination.

Within the Practical Exam category, the following assignments need to be completed:

Report practical sessions with a relative weight of 1 which comprises 35% of the final mark.

Note: Reports describing the results on the assignments given during the practical sessions will be evaluated. Evaluation will be based on the correctness of the performed assignments, the answers to the questions and the completeness of the reports.

Additional info regarding evaluation

Students must participate to the oral exam and complete the reports on the practical sessions. Students must pass both parts (oral exam and practical sessions) in order to pass the course. An exemption for either part can be obtained for the second session, if a passing grade was obtained for that part in the first session.

Allowed unsatisfactory mark

The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context