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Høst 2023
FYS-3032 Health data analytics - 10 stp
The course is administrated by
Institutt for fysikk og teknologi
Type of course
The course is available as a singular course. The course is also available to exchange students and Fulbright students.
The course will only be taught if there are sufficiently many students. Please contact the student adviser as soon as possible if you are interested in following the course.
Course overlap
FYS-8032 Health data analytics 8 ects
Course contents
The course will study machine learning methods and algorithms used for analysing and interpreting the vast amounts of data acquired within the healthcare system. Focus will be on information extraction by pattern analysis and statistical inference from health data in order to derive clinically relevant decision support systems. The course will in addition to machine learning algorithms contain elements of image processing, pattern recognition and statistics. It has a significant practical component, in which various applications will be discussed.
Admission requirements
Admission requirements are a Bachelor's degree in physics or similar education, including specialization in physics worth the equivalent of not less than 80 ECTS credits. Local admission, application code 9371 - singular course at Master's level.
Objective of the course
Knowledge - The student can:
- Describe fundamental sources and principles behind data acquisition broadly within the health domain (examples include but are not limited to imaging techniques such as PET/MR, electronic health records, wearable sensors, and biological data).
- Describe a number of decision support system application areas within healthcare
- Discuss and select appropriate health data sources and modes applicable to a given application or problem setting
- Discuss and select appropriate approaches within health data analytics when it comes to the choice of machine learning algorithm to use, pre-processing and post-processing techniques to use
Skills - The student can:
- explain the application domains of machine learning and data analysis methodology and algorithms with respect to decision support systems in health
- analyse health data for decision support by applying various machine learning methods and algorithms, including feature extraction (for instance by image processing methods), and statistical inference.
General expertise - The student can:
- give a basic interpretation of data acquisition within the healthcare system and interpret the role of the data within the context of decision support systems
- implement and apply machine learning methods and algorithms for analysis of health data in e.g. Python for the purpose of decision support
Language of instruction
The language of instruction is English and all of the syllabus material is in English. Examination questions will be given in English but may be answered either in English or a Scandinavian language.
Teaching methods
Lectures: 30 hours Exercises: 30 hours