AI in Health
AI techniques applied to the medical field: diagnosis, treatment, monitoring, and prevention. Ontologies, explainable ML, guidelines.
Learning objectives
The aim of the course is to make the students familiar with a number of Artificial Intelligence techniques (Knowledge and Understanding) and how they can be applied to the medical field (Applying Knowledge and Understanding, Making Judgments). Furthermore, students should have capability to work in teams (Communication).
The aim of the course is to make the students familiar with a number of Artificial Intelligence techniques and how they can be applied to the medical field. The course will be structured along the patient journey: diagnosis, treatment, monitoring, and prevention. Several AI techniques will be introduced during the course. The assignments offer students the opportunity to gain practical experience with AI techniques in the health domain. In the assignments, both the AI-technique and a medical topic have to be applied in a practical, hands on, way. The course will be structured into three thematic modules. module 1: Diagnosis (ontologies, explainable ML) module 2: Treatment (representations of guidelines and quality of care indicators, neurosymbolic approaches) module 3: Monitoring & Prevention Beside these modules there might be a couple of guest lectures, on for instance natural language processing for medical texts, bioinformatics, and ethical impact.
Assessment
Practical group assignments for each module, and an individual exam. Exam (50%) and practical assignments (50%) form the final grade. For both parts the grade needs to be sufficient (5.5 or higher) to obtain a final grade. No resit is possible for the practical assignments.
Teaching methods
There will be two lectures per week and one practical session per week.
Literature
Selected scientific papers.
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AI in Health
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