autumn 2023
FYS-3032 Health data analytics - 10 ECTS

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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.

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.

Course overlap

If you pass the examination in this course, you will get an reduction in credits (as stated below), if you previously have passed the following courses:

FYS-8032 Health data analytics 8 ects

Course content

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.

Recommended prerequisites

FYS-2006 Signal processing, FYS-2010 Image Analysis, FYS-2021 Machine Learning, FYS-3012 Pattern recognition, STA-2003 Time series, STA-3002 Multivariable Statistical Analysis

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

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 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

Information to incoming exchange students

This module is open for exchange students with a Bachelor's degree in physics or similar education.

This course is open for inbound exchange student who meets the admission requirements. Please see the Admission requirements section.

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: https://en.uit.no/education/art?p_document_id=510412

Error rendering component

More info about the coursework requirements

Up to two mandatory assignments.

Re-sit examination

There is no access to a re-seat examination in this course.
  • About the course
  • Campus: Tromsø |
  • ECTS: 10
  • Course code: FYS-3032
  • Earlier years and semesters for this topic