LEARNING OUTCOMES
Upon successful completion of this course, the student:
- Understands and can independently formulate data-analysis requirements associated with different healthcare decision making tasks (risk assessment, early diagnosis, differential diagnosis, treatment planning, and treatment follow-up)
- Can recognize what kind of approaches (data-driven, rule-based, mechanistic models etc) are most appropriate for what decision-making challenge
- Has knowledge of, and knows how to select and apply methods for data curation and quality assurance
- Has an understanding of the most common feature extraction and feature selection methods
- Has gained knowledge of the most common AI/ML methods for advanced decision support
- Understands how to objectively assess the performance of AI/ML methods in common healthcare decision support settings
- Understands considerations such as explainability, privacy-preservation, bias and ethics in computer-based decision support in health and know which tools are available to address these.
Credits: 5
Schedule: 23.10.2023 - 29.12.2023
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Marcus van Gils
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
See Learning outcomes
Assessment Methods and Criteria
valid for whole curriculum period:
exercises, exam
Workload
valid for whole curriculum period:
lectures, exercises
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language : English
Teaching Period : 2022-2023 Autumn II
2023-2024 Autumn II