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?

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 TBA

Organizers:

• Associate Professor Rasmus Froberg Brøndum, rfb@dcm.aau.dk
• Professor Martin Bøgsted, m_boegsted@dcm.aau.dk
• Associate Professor Louise Pape-Haugaard, lph@hst.aau.dk

Lecturers:

• Associate Professor Rasmus Froberg Brøndum, rfb@dcm.aau.dk
• Assistant Professor Charles Vesteghem
• Associate Professor Louise Pape-Haugaard, lph@hst.aau.dk
• Professor Thomas Moeslund
• Associate Professor Lasse Riis Østergaard
• Senior Statistician Jan Brink Valentin
• Chief Executive Officer Mads Lause Mogensen
• Other experts

ECTS: 2.5

Time: 24, 25 and 26 June 2024 (08:15-16:15)

Place: TBA

Zip code: 9220

City: Aalborg

Number of seats: 30

Deadline: 3 June 2024

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