Fundamentals of Clinical Data Science (2024)

Welcome to Fundamentals of Clinical Data Science

PhD Program: Biomedicine (B)

Description: 

Clinical data science can be defined as the scientific field, which turns healthcare data into clinically useful applications. This course will introduce the disciplines involved in the full value chain of clinical data science, covering the transformation of data to model and to applications, with an aim of giving an overview and understanding of the processes, rather than how to perform them. The course is organized into three major themes:

1)    Data sources: The first part of the course covers the management and collection of data from both public sources, national registries or trough case report forms designed for a study. We will introduce both how to access data, how to handle privacy concerns (GDPR) and how to make your own data useable for others (FAIR principles).

2)    Modelling: The second part of the course teaches how to transform the collected data from possibly multiple sources to input for a predictive model, and how to train and validate a model using techniques such as classification, regression, or clustering.

3)    From model to clinic: The final part of the course deals with turning a validated model into a clinical decision support system to strengthen operational excellence in value-based health care. How do we ensure that data is available in real time? What legal barriers or ethical issues are involved when a medical decision is guided by artificial intelligence?

Organizers: 

Rasmus Froberg Brøndum, Associate Professor, Center for Clinical Data Science, Department of Clinical Medicine, rfb@dcm.aau.dk

Martin Bøgsted, Professor,  Center for Clinical Data Science, Department of Clinical Medicine, m_boegsted@dcm.aau.dk

Clara Bender, Assistant Professor, Department of Health Science and Technology,  csch@hst.aau.dk

Lecturers: 

Rasmus Froberg Brøndum, Associate Professor, Center for Clinical Data Science, Department of Clinical Medicine, AAU

Charles Vesteghem, Assistant Professor, Center for Clinical Data Science, Department of Clinical Medicine, AAU

Clara Bender, Assistant Professor, Department of Health Science and Technology, AAU

Andreas Møgelmose, Associate Professor, Department of Architecture, Design and Media Technology, AAU

Lasse Riis Østergaard, Associate Professor, Department of Health Science and Technology, AAU

Jan Brink Valentin, Senior Statistician, Danish Center for Clinical Health Services Research, AAU

Susanne Andersen, Academic Officer, Grants & Contracts, AAU

Mads Lause Mogensen, Chief Executive Officer, Treat Systems

Roman Jurowetzki , Associate Professor, Aalborg University Business School 

ECTS: 2.5

Dates: June 24 – 26, 2024 (3 days)

Time: 8:30 – 16:00

Place: AAU SUND, Room 11.00.032, Selma Lagerløfs Vej 249, 9260 Gistrup,

Deadline: 3 June 2024

Literature/Requirements: Students are expected to have some experience with collecting and analyzing health care data. Suggested reading: Kubben, P., Dumontier, M., Dekker, A. (editors) Fundamentals of Clinical Data Science. Springer Open, 2019. Available online at: link


Program

Lectures are divided into two morning lectures and two afternoon lectures. Each lecture block includes group discussions, where students will try to apply the principles to their own case.

Day 1: Intro and data sources (Monday, June 24)

08.30 – 09:15 Introduction to course (Rasmus Brøndum)

09:15 – 10.00: Introduction of cases for exercise (and group work)

10.00 – 10.30: Break

10.30 – 12:00: Collection of data from registries and RedCAP (Charles Vesteghem)

12.00 – 12.30: Lunch Break

12.30 – 14.00: Extraction of information from unstructured data using NLP (Roman Jurowetzki)

14.00 – 14:30: Break

14:30 – 15.30:  Legal requirements for starting a data science project (Susanne Andersen)

 

Day 2: Modelling (Tuesday, Jun 25)

08.30 – 10.00: Predictive models and validation (Jan Valentin)

10.00 – 10.30: Break

10.30 – 12.00: Modelling image data (Lasse Riis Østergård)

12.00 – 12.30: Lunch Break

12:30 – 14.30: Deep learning and explainable AI + break + Ethics (Andreas Møgelmose)

14.30 – 15:30: Break + group work


Day 3: From model to clinic (Wednesday,  June 26)

08.15 – 09:00:  Case story: TREAT systems (Mads Lause)

09:15 – 10:00: Introduction of final exercise and how to build a mock-up (Clara Bender)

10.00 – 10.30: Break

10.30 – 11:15: Group work: Low-Fi mock-up

11:15 – 12:00: Group work: Quick pitches, feedback and re-design of prototypes

12.00 – 12.30: Lunch Break.

12.30 – 13.30: Group work: High-Fi mock-up.

13:30 - 15:00: Group presentations of final prototypes.

15.00 – 15.30: Course wrap up. Break, cake and evaluation


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registration.

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at aauphd@adm.aau.dk