autumn 2022
FYS-3032 Health data analytics - 10 ECTS
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 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.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
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
Examination
| Examination: | Date: | Weighting: | Duration: | Grade scale: |
|---|---|---|---|---|
| Off campus exam | 24.10.2022 09:00 (Hand out) 10.11.2022 13:00 (Hand in) |
4/10 | 4 Weeks | A–E, fail F |
| Oral exam | 6/10 | 40 Minutes | A–E, fail F | |
Coursework requirements:To take an examination, the student must have passed the following coursework requirements: |
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| Mandatory assignment | Approved – not approved | |||
| Mandatory assignment | Approved – not approved | |||
- About the course
- Campus: Tromsø |
- ECTS: 10
- Course code: FYS-3032
- Responsible unit
- Institutt for fysikk og teknologi
- Kontaktpersoner
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- Tidligere år og semester for dette emnet