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.

Programstudents may register for the course through Studentweb. The registration deadline is September 1st/February 1st.

Other PhD students at UiT and external applicants may apply for admission through Søknadsweb, application code 9301. The application deadline is June 1st for the autumn semester and December 1st for the spring semester

Admission requirements

PhD students or holders of a Norwegian master´s degree of five years or 3+ 2 years (or equivalent) may be admitted. PhD students must upload a document from their university stating that there are registered PhD students. This group of applicants does not have to prove English proficiency and are exempt from semester fee. Holders of a Master´s degree must upload a Master´s Diploma with Diploma Supplement / English PhD students at UiT The Arctic University of Norway register for the course through StudentWeb . External applicants apply for admission through SøknadsWeb. Application code 9303.

All external applicants have to attach a confirmation of their status as a PhD student from their home institution. Students who hold a Master of Science degree, but are not yet enrolled as a PhD-student have to attach a copy of their master's degree diploma. These students are also required to pay the semester fee.

Course content

Recommended prerequisites

Objectives 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
• Learn about advanced machine learning methodology for analysing health data
• Understand the limitations of state of the art algorithms in health analytics and be aware of recent trends at the frontier of research in this field

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. Matlab or Python for the purpose of decision support
• implement and apply specialized and advanced machine learning methods for analysis of health data in e.g. Python for the purpose of decision support

Language of instruction and examination

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