Kursusinformation

Couse name: Fundamentals of Clinical Data Science (2023)

PhD Program: Medicine, Biomedical Science and Technology

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, Associate Professor, Center for Clinical Data Science and Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU, rfb@dcm.aau.dk

Martin Bøgsted, Professor,  Center for Clinical Data Science and Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU, m_boegsted@dcm.aau.dk

Louise Pape-Haugaard, Associate Professor, Department of Health Science and Technology,  lph@hst.aau.dk

Lecturers: 

Rasmus Froberg Brøndum, Associate Professor, Center for Clinical Data Science and Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU

Charles Vesteghem, Assistant Professor, Center for Clinical Data Science and Center for Molecular Prediction in Inflammatory Bowel Disease, Department of Clinical Medicine, AAU

Louise Pape-Haugaard, Associate Professor, Department of Health Science and Technology, AAU

Thomas Moeslund, 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

Lone Frøkjær Christensen, Research Data Consultant, Research Data and Statistics, Aalborg UH

Mads Lause Mogensen, Chief Executive Officer, Treat Systems

Tomer Sagi, Assistant Professor, Department of Computer Science, AAU

Thomas Ploug, Professor, Department of Communication and Psychology, AAU

ECTS: 3

Dates: Feb 27 – Mar 1, 2023 (3 days)

Time: 8:30 – 16:00

Place: AAU SUND, Selma Lagerløfs Vej 249, SLV249 12.02.066  all days

Zip code:  9260

City:  Gistrup

Number of seats: 30

Deadline: 15 February 2023

Important information concerning PhD courses:

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

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 3.000 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 registrations.