In this hands-on course, you will learn how to develop and apply reliable prediction models in clinical practice. You’ll gain insight into the entire modelling process: from designing a model to assessing its performance and conducting both internal and external validation.
You’ll also be introduced to a range of machine learning techniques, including Lasso regression and tree-based methods.
The programme combines intensive, interactive lectures with computer-based practicals. During these sessions, you’ll work with realistic healthcare data using widely used software such as R, RStudio, and SPSS. On the first day, you’ll receive an introduction to working with R and RStudio, enabling you to start building and testing prediction models right away.
Key Takeaways:
- Gain knowledge and insight into developing clinically relevant prediction models
- Apply logistic regression and Cox proportional hazards models
- Get introduced to advanced machine learning techniques
- Use Lasso regression and tree-based methods
- Understand model development for clinical decision-making
Start Date: 8 January 2026
Tuition fee: € 1,450 – includes lunch, coffee, and tea
Duration: 4 days
Time Commitment: 20–40 hours (exam optional)
Study Load: 2 ECTS
Location: Amsterdam, Frans Otten Stadium
Registration Deadline: 15 December 2025
Accreditation (if applicable): This course is accredited only for Dutch professionals: general practitioners, geriatric specialists, doctors for the mentally handicapped, medical specialist, social physicians, company doctors, insurance doctors, doctors for society and health.
The course “Clinical Prediction Models and Machine Learning” (WK80) is accredited for 20 hours.
To qualify for these credits, there is an attendance requirement of 100%.