Students
- learn methods to extract, link and prepare structured and unstructured data from health registers, patient information systems, pharmacy records, health records.
- learn about legal and ethical constraints and how to use privacy enhancing technologies (such as pseudonymisation) to address these constraints
- learn to define which information is required to be able to determine a specific measurement of the effectiveness of an intervention in health care or public health
- learn to retrieve this information from existing observational registries, big data repositories and registry based trials
- learn which provisions to take to deal with legal and ethical issues concerning the use of big data
- learn which methods to use to answer causal questions about the effect of intervention on observational big data
- are familiar with concepts of evaluating probabilistic prediction models, such as discrimination and calibration, and how to asses them using cross-validation
- have profound knowledge of the reasons for over-fitting and complete separation with high-dimensional data
Assessment
There are 3 exams in total during this course.
• 2 assignments in weeks 3 and 5 of the course each counting respectively for 30% of the grade.
• 1 case study, to be handed in at the end of week 10 of the course counting for 40% of the grade.
The average weighted grade will be your final score for this course and the one entered into Osiris.
For a retake the mark of the original test needs to be at least a 4.
Prerequisites
INFOMDWR Data Wrangling
This course is for students in the master Applied Data Science only.
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Course form
Lectures, exercises, assignments.
Literature
Chapters and articles, listed in the course manual.
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