Health data analytics

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.

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.

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

Anbefalte forkunnskaper

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

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

Language of instruction and examination

Schedule

This course is open to incoming exchange students.

Study Level: Master's

Prerequisites:
To be admitted to this course, you must hold a Bachelor’s degree in physics or a closely related field. Your degree must include at least 80 ECTS credits of specialization in physics.
For full details, please see the Admission requirements section.

For details on how to apply for exchange, course selection guidelines, or to contact the Incoming Admissions Team, please visit: Admissions for Student Exchange.

More info about the coursework requirements

Re-sit examination